CN113763367A - Comprehensive interpretation method for infrared detection characteristics of large-size test piece - Google Patents

Abstract

The invention discloses a comprehensive interpretation method for infrared detection characteristics of a large-size test piece, which comprises the following steps: acquiring an infrared thermal image sequence of the large-size test piece from infrared detection, and acquiring an infrared thermal reconstruction image of the large-size test piece from the infrared thermal image sequence; performing image down-sampling on the typical type defect infrared thermal reconstruction image in the large-size impact test piece to obtain a down-sampling thermal image containing lower infrared thermal radiation data amount, and executing a multi-target guiding filtering weight acquisition layer step based on the down-sampling thermal image; and performing a multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer based on the multi-target optimal weight ratio parameter, and finally combining the weighted average base layer thermal image and the detail layer thermal image to obtain a final fusion detection infrared thermal image. The invention improves the clustering efficiency, reduces the overall detection time of the detection algorithm, improves the detection performance of single infrared thermal images and solves the problem of incomplete defects of the single detected images.

Description

Comprehensive interpretation method for infrared detection characteristics of large-size test piece

Technical Field

The invention belongs to the technical field of equipment defect detection, and particularly relates to a comprehensive interpretation method for infrared detection characteristics of a large-size test piece.

Background

The pressure vessel is widely applied to the fields of aerospace, energy chemical industry, metallurgical machinery and the like, such as rocket fuel storage tanks, space station sealed cabins and the like, and is used for containing flammable and combustible liquid or gas with certain pressure, so that the safety detection of the pressure vessel is very important. Common defect types of the pressure container comprise fatigue crack defects, welding defects, corrosion defects and the like, and corresponding conventional detection means are mature. However, it is very difficult to detect defects in a large pressure vessel having an inner diameter of 2 m or more rapidly and precisely in all directions. The infrared thermal imaging detection technology is an effective non-contact nondestructive detection method for large-scale pressure vessel damage defects, and structural information of the surface and subsurface of a material is obtained by controlling a thermal excitation method and measuring the temperature change of the surface of the material, so that the purpose of detection is achieved. When acquiring the structural information, a thermal infrared imager is often used for recording the temperature field information of the surface or the sub-surface of the test piece along with the time change, and converting the temperature field information into a thermal image sequence to be displayed. And analyzing and extracting the characteristics of the transient thermal response of the thermal image sequence to obtain a reconstructed image capable of characterizing and strengthening the defect characteristics, thereby realizing the detection and interpretation of the defect. Although the reconstructed thermal image has good detectable performance when representing a certain defect damage area characteristic, when the reconstructed thermal image is applied to the damage defect detection of the large-size pressure container, all the defects of the whole large-size pressure container cannot be simultaneously obtained through single detection due to the limitation of detection conditions. Therefore, the large-sized pressure container needs to be subjected to multiple infrared detections in different regions, so that a comprehensive and accurate detection result is obtained.

In the invention, after the BIRCH clustering algorithm and the DPC clustering algorithm based on density are respectively utilized to improve the algorithm clustering efficiency, what is more important is how to enable the detection image to simultaneously represent the defect characteristics of different areas obtained in multiple detections. In order to compensate the limitation of a single reconstructed thermal image in the characterization of the overall defect characteristics of a large-size pressure vessel, it is a good way to fuse the thermal characteristics of defects contained in a plurality of thermal image sequences by using an infrared thermal image fusion algorithm. The infrared thermal image fusion integrates the thermal radiation characteristics of different areas and different types of defects in a plurality of reconstructed thermal images in different thermal image sequences, and the thermal radiation characteristics are fused into one fused thermal image, so that the ability of simultaneously representing the characteristics of the different areas and different types of defects obtained through multiple detections is given to one fused thermal image, and the method is a mode for effectively improving the ability of detecting the complex type defects by using a single infrared reconstructed thermal image. Therefore, it is a challenging issue to fuse different regions and different types of lesion thermal images with high quality. In general, the infrared thermal image fusion technology only considers the relatively obvious defect characteristic information in the thermal image when fusing the infrared reconstruction thermal image, and does not consider the condition that a plurality of small-sized holes and hollow damages exist in the test piece. So that the fine crack defects in the fused thermal image are smoothed out as noise, which is fatal to the safety of the pressure vessel. In the defect feature extraction of the large-size pressure container, image edge and texture information of the defect are one of the very important features for quantitatively identifying the defect. The smoothed fine defects directly affect the accuracy of defect quantitative analysis, resulting in defect omission and detection integrity degradation. Therefore, in the infrared thermal image fusion process of the defect detection of the large-size pressure container, a plurality of fusion targets and requirements should be considered simultaneously, the retention requirement of the large-size defect characteristics is required to be included, and the detail retention and enhancement of the tiny defect and the background information smoothing effect of the non-defect area of the fusion image should be considered.

Therefore, the invention introduces an image fusion technology based on the combination of double-layer multi-objective optimization and guided filtering so as toThe fusion function of a plurality of thermal images is rapidly realized, so that the detection image can integrate defect information in a plurality of thermal image sequences, the characteristic conditions of different areas and different types of defects in the large-size pressure container are represented, and the high-quality imaging function of the whole defect condition of the large-size pressure container is realized. Guided filtering is a novel edge-preserving filter that is capable of preserving edge information of an image while smoothing the image. Therefore, the guided filtering is very suitable for the requirement of spacecraft defect detection. And the multi-objective evolutionary optimization algorithm can synergistically optimize the vector optimization problem. The method combines the double-layer multi-objective optimization and guided filtering technology, firstly greatly reduces the data amount required by the multi-objective optimization by utilizing the downsampling operation, performs a multi-objective optimization algorithm on the downsampled thermal image retaining the important defect information of the test piece, and obtains the targeted optimal guided filtering linear transformation coefficient a by utilizing the multi-objective simultaneous optimization of a plurality of guided filtering cost functions_kAnd b_k. Therefore, the advantages of the multiple guiding filters are combined, the large-size edge retention characteristic of edge perception weighted guiding filtering, the detail retention characteristic of gradient domain guiding filtering and the noise removal characteristic of LoG guiding filtering are considered, and the guiding filtering after multi-objective optimization can be combined with the advantages of multiple different guiding filtering cost functions with filtering preference. And based on the optimal weighting weight of the multi-target guided filtering obtained on the down-sampling thermal image, returning the weighting parameter to the upper layer, thereby carrying out the optimal multi-target guided filtering on the original reconstructed thermal image without down-sampling. The filtered image can furthest reserve large-shape edge characteristics and places with violent image gradient changes in the original infrared thermal image, retain the textures and forms of a plurality of tiny crack defects in the pressure container, and simultaneously smoothen the image of a background area without the defects in the infrared thermal image and remove noise information. The filtering performance is further improved, so that the infrared thermal image fusion performance is improved, and the detection and defect extraction performance of the algorithm for the whole defects of the large-size pressure container is improved.

Disclosure of Invention

An object of the present invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.

To achieve these objects and other advantages in accordance with the purpose of the invention, a method for comprehensive interpretation of infrared detection characteristics of a large-sized test piece is provided, comprising the steps of:

the method comprises the following steps of firstly, carrying out infrared detection on a large-size test piece to obtain an infrared thermal image sequence of the large-size test piece, and obtaining an infrared thermogravimetric image of the large-size test piece from a plurality of infrared thermal image sequences by utilizing an infrared feature extraction and infrared thermal image reconstruction algorithm;

performing image down-sampling on the infrared thermogravimetric image of the large-size test piece defect area to obtain a down-sampled infrared thermal image containing lower infrared thermal radiation data quantity, and acquiring a thermal amplitude fusion coarse weight map of the down-sampled thermal image based on the down-sampled infrared thermal image; performing multi-objective optimization guided filtering based on the down-sampling thermal image and the down-sampling fusion coarse weight map to obtain a Pareto optimal weight vector; modeling a filter input and filter output relation of the multi-target guiding filter; performing multi-objective optimization problem modeling on linear transformation parameters of the guided filtering to obtain a final leading edge approximate solution set of the linear parameters of the multi-objective guided filtering; optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm; selecting a guide filtering linear transformation coefficient compromise solution with the maximum weighting membership degree from the optimal Pareto optimal solution set based on a weighting membership degree scheme, recording a corresponding optimal weight vector group, thus obtaining the optimal weight ratio of a plurality of comprehensive guide filters, and then transmitting the optimal weight parameters to an original infrared thermal image fusion layer;

thirdly, performing a multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer based on the multi-target optimal weight ratio parameter; decomposing the infrared thermal reconstruction image of the defect area in the large-size impact test piece into a base layer infrared thermal image and a detail layer infrared thermal image; calculating to obtain an initial infrared thermal radiation coarse fusion weight map; acquiring a multi-target oriented filtering optimal filtering operator of an original infrared reconstruction thermal image layer; performing multi-target guiding filtering on the infrared thermal amplitude fusion coarse weight graph of the obtained infrared detection area infrared thermal reconstruction image by using an optimal guiding filtering operator obtained by multi-target optimization to obtain corrected infrared thermal amplitude fusion weight images of the basic layer and the detail layer; based on the obtained refined detail layer thermal amplitude fusion weight map and the base layer thermal amplitude fusion weight map, detail layer infrared thermal image information and base layer infrared thermal image information among typical type defect infrared thermal reconstruction images of the large-size test piece are fused to obtain a plurality of base layer infrared thermal images and detail layer infrared thermal images fused with effective information of the infrared thermal reconstruction images of the multiple detection areas, and finally the base layer infrared thermal images and the detail layer infrared thermal images after weighted averaging are combined to obtain a final fusion detection infrared thermal image.

Preferably, the step one of acquiring an infrared reconstructed thermal image from the infrared thermal image sequence by using an infrared feature extraction and infrared thermal image reconstruction algorithm specifically comprises the following steps:

s11, extracting characteristic information of each transient thermal response from a thermal image sequence S acquired by a thermal infrared imager and forming a characteristic matrix Fe; wherein S (I, J, T) represents pixel values of an ith row and a jth column of a T-frame thermal image of the thermal image sequence, T is 1.. T, T is a total frame number, I is 1.. T, I is a total row number, and J is 1.. J, J is a total column number; fe (i, j, f) represents the f (f is 1, …,6) th feature information corresponding to the coordinate position of the i-th row and the j-th column of the feature matrix; the first characteristic information is a thermal amplitude peak value, namely Fe (i, j,1) is max (S (i, j, and): S (i, j, and) represents the temperature change condition of the ith row and the jth column in the whole T frame process; the second characteristic information being the mean value of the thermal amplitudes, i.e.

The 3 rd feature information is coefficient of variation, i.e.

The 4 th characteristic information being the rate of rise, i.e.

Wherein t is_maxIndicating the peak of thermal radiationThe number of frames corresponding to the value; the 5 th characteristic information being the rate of descent, i.e.

The 6 th characteristic information is the heat radiation kurtosis and is used for representing the peak or the flatness of the transient thermal response curve, namely

Finally obtaining a characteristic matrix Fe of the thermal image sequence S;

step S12, self-adaptively clustering the feature matrix Fe into | C | classes by using a BIRCH clustering algorithm; constructing a triple clustering characteristic CF, wherein CF is { N, LS, SS }, wherein N is the number of sample points owned by the node, LS is a sum vector of characteristic dimensions of the sample points owned by the node, and SS represents a square sum of the characteristic dimensions of the sample points owned by the node; generating a clustering feature tree based on the CFs, defining the maximum CF number B of the internal nodes, the maximum CF number L of the leaf nodes, and the maximum sample radius threshold T of each CF of the leaf nodes; continuously searching for clustering characteristics meeting the requirement within a hypersphere radius threshold T from a root node, creating a new leaf node under the condition that the number of leaf nodes is less than L, and putting a new sample meeting the condition; judging, checking and splitting leaf nodes to finally obtain a clustering feature tree; screening the clustering feature tree to remove abnormal CF nodes; using a global clustering algorithm to perform clustering repair on all leaf nodes to obtain a clustering feature tree; taking the global clustering center point as a seed, redistributing the data points to the nearest seed, ensuring that repeated data are distributed into the same cluster, and adding a cluster label; synchronizing the clustering Cluster labels to each transient thermal response in the original thermal image sequence to form a Cluster [ h ], wherein h is 1,2,., | Num _ Cluster | represents a category label, and | Num _ Cluster | represents the total number of categories;

step S13, respectively extracting typical characteristic transient thermal responses from different clusters and reconstructing a thermal image based on the typical characteristic transient thermal responses; calculating the clustering center of each category in the clustering result as the typical characteristic transient thermal response of each category of defects:

wherein

Cluster result h for h]The kth of | Num _ Cluster | represents the transient thermal response, Cluster [ h ═ 1]Forming a matrix Y by typical transient thermal responses of various types of defects for the total number of transient thermal responses contained in the h-th clustering result;

the infrared thermal image reconstruction is carried out by utilizing the information of the matrixes Y and S, each frame image of S is extracted into a column vector according to columns and is arranged in time sequence to form an I multiplied by J row and T column two-dimensional image matrix O, and a reconstruction matrix R is obtained based on the following transformation formula:

wherein the content of the first and second substances,

is a matrix of | Num _ Cluster | xT, which is a pseudo-inverse of the matrix Y, O^TThe method is characterized in that the method is a transpose matrix of a two-dimensional image matrix O, and an obtained reconstruction matrix R is | Num _ Cluster | rows and I multiplied by J columns; intercepting each row of the reconstruction matrix R to form an I multiplied by J two-dimensional image to obtain a Num _ Cluster I multiplied by J two-dimensional image, wherein the images are infrared reconstruction thermal images containing different thermal response area characteristic information, and recording a non-defect background area reconstruction thermal image in the images as a_BAnd R, recording the reconstructed thermal images corresponding to the transient thermal responses of all the class characteristics_iR, i ═ 1., | Num _ Cluster |; wherein each infrared reconstructed thermal image contains, in addition to the background area thermal image of the defect-free lesion, thermal reconstruction information characteristic of one type of defect of the complex type.

Preferably, in the step, a plurality of infrared detections are performed on the large-size test piece to obtain a plurality of thermal image sequences of the large-size test piece, and a plurality of reconstructed infrared thermal images of the large-size test piece are obtained from the plurality of thermal image sequences by using an infrared feature extraction and infrared thermal image reconstruction algorithm, and the specific method includes:

step S11, using a three-dimensional matrix set { S } for a plurality of thermal image sequences acquired from a thermal infrared imager¹,...,Sⁱ,...,S^|C|Denotes wherein SⁱRepresenting a thermal image sequence obtained by an infrared thermal imager in the ith infrared detection, and | C | representing the total number of the thermal image sequences; sⁱ(M, N, T) represents a temperature value at the coordinate position of the mth row and the nth column of the tth frame thermal image in the ith thermal image sequence, wherein T is 1, the.

Step S12, for the ith thermal image sequence SⁱExtracting the ith thermal image sequence S by utilizing a transient thermal response data extraction algorithm based on block variable step lengthⁱTransient thermal response data set X of mesovalueⁱ(g) (ii) a Passing the ith thermal image sequence S through a thresholdⁱDecomposition into K different data blocks_kSⁱ(m ', n', t) wherein k represents the ith thermal image sequence SⁱM ', n', t respectively represent temperature values at the coordinate positions of the m 'th row, the n' th column and the t-th frame of the kth sub-data block, and then define the ith thermal image sequence S according to the temperature change characteristics in different data blocksⁱStep size of search line in k-th data block_kRSSⁱAnd column step size_kCSSⁱ(ii) a Based on different search steps, K1, K, in different data blocks, comparing correlation coefficients between data points, and searching for a series of correlation coefficients greater than a threshold THC_crAnd adding the ith thermal image sequence SⁱTransient thermal response data set X in (1)ⁱ(g)；

Step S13, using DPC clustering algorithm based on density peak to classify the ith thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) Adaptive clustering of transient thermal responses in (1); firstly, randomly calculating the distance between two transient thermal response samples; calculating the local density rho of each transient thermal response sample according to the truncation distance_i(ii) a Calculating each transient thermal response sample to a local density greater than it anddistance delta from nearest transient thermal response sample point_i(ii) a Using p_iAnd delta_iDrawing a decision graph and dividing rho_iAnd delta_iThe points that are all relatively high are marked as cluster centers, p_iRelatively low but delta_iRelatively high points are marked as noise; distributing the rest transient thermal response sample points to the nearest neighbor cluster of the sample points with the density larger than that of the sample points to obtain the final transient thermal response cluster division, and dividing the thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) Adaptive clustering to form a set of clusters

Wherein H represents a defect type label, and H represents the total number of types of complex defects existing in the current infrared detection area;

step S14, respectively extracting representative characteristic transient thermal responses of various complex defects in the ith detection area from different clusters and reconstructing thermal images based on the transient thermal responses; calculating the clustering center of each category in the clustering result as the representative characteristic transient thermal response of each category of defects:

wherein

For the h-th clustering result_X(g)Cluster[h]H-1, …, the kth transient thermal response in H_X(g)Cluster[h]L is the total number of transient thermal responses contained in the h-th clustering result, and a matrix Y is formed by the representative transient thermal responses of all the types of defectsⁱ；

Using matrix YⁱAnd SⁱThe information is subjected to infrared thermal image reconstruction, and the ith thermal image sequence S is obtainedⁱEach frame image is extracted into a column vector according to columns and arranged in time sequence to form a two-dimensional image matrix O of M multiplied by N rows and T columnsⁱObtaining a heat amplitude value reconstruction matrix R of the ith detection based on the following transformation formulaⁱ：

Wherein the content of the first and second substances,

is H × T matrix, and is a representative transient thermal response matrix YⁱPseudo-inverse matrix of (O)ⁱ)^TIs a two-dimensional image matrix OⁱTranspose matrix, obtaining reconstruction matrix of H rows and M multiplied by N columns, intercepting reconstruction matrix RⁱForming an M multiplied by N two-dimensional image for each line to obtain H M multiplied by N two-dimensional images, namely reconstructing thermal images containing different thermal response area characteristic information in the thermal image sequence obtained by the ith infrared detection, and recording the non-defect background area reconstruction thermal images as_BR, recording the reconstructed thermal image corresponding to each type of defect area as_hR, H1, wherein each reconstructed thermal image contains, in addition to the thermal image of the background area free of defect lesions, the characteristic thermal reconstruction information of one type of defect among the complex types of defects currently detected, and the reconstructed thermal image of the type of defect in the detected area obtained in the ith infrared detection is recorded as the reconstructed thermal image of the type of defect in the detected area_Def.(i)R；

Step S15, if i < | C |, i +1 and step S12-step S14 are repeated until all types of defect reconstruction thermal images in the current detected area are respectively obtained from a plurality of thermal image sequences obtained by multiple detections, PSNR values of all types of defect reconstruction thermal images in the current area are calculated, typical type defect reconstruction thermal images in all detection areas are selected based on the peak signal-to-noise ratio (PSNR) maximum principle, and a typical type defect reconstruction thermal image set in each detection area of the large-size test piece is obtained_Def.(1)R,…,_Def.(i)R,…,_Def.(C)R }, wherein_Def.(i)R represents a typical type of defect reconstruction thermal image of the detected region in the ith thermal image sequence, i 1.

Preferably, wherein the step is conducted with background area heat removed(C-1) infrared reconstructed images other than image₁R,…,_iR,…,_|C|-1R, down sampling each image to obtain a down sampled thermal image containing a lower amount of infrared thermal radiation data₁R^down…,_iR^down,…,_|C|-1R^downAnd the size of the down-sampled thermal image is I 'xJ', and the following multi-target oriented filtering weight acquisition layer steps are executed based on the down-sampled thermal image, wherein the specific method comprises the following steps:

step S21, based on the down-sampling infrared thermal image_iR^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_iP^down：

_iH^down＝_i R^down*L

_iS^down＝|_iH^down|*GF

Wherein L is a laplacian filter; non-viable cells_iH^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in the down-sampling thermal image based on the following formula_iP^down：

Wherein the content of the first and second substances,

for downsampling coarse weight maps_iP^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,

is composed of_iP^downThe thermal amplitude value of the kth coordinate point of (a) is fused with a weight value, k 1, I 'x J',

is a heat amplitude significance characteristic diagram_iS^downCorresponding to the k-th coordinate pointThe level of radiation significance of, k 1.., I 'x J';

step S22, making a picture based on the downsampled thermal image₁R^down…,_iR^down,…,_{|Num_Cluster|-1}R^downGreat weight map of integration of } and downsampling₁P^down…,_iP^down,…,_{|Num_Cluster|-1}P^downPerforming multi-objective optimization guided filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: sampling thermal images in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. down-sampling infra-red thermal images_iR^downAt the kth coordinate point of

A centered local rectangular window, k is 1., I 'x J', which has a size of (2r +1) × (2r +1), the input-output relationship of the multi-target guided filtering is:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downDownsampled output image obtained by performing multi-target guided filtering on input image_iO^downThe nth coordinate point of (a), n is 1, and I 'x J';

is composed of_iR^downThe downsampled reconstructed image thermal amplitude value corresponding to the nth coordinate point of (a), n is 1. a is_kAnd b_kIs shown in

Centered guided filter window w_kLinear transformation parameters of (I), k ═ 1., I 'x J';

step S222, in order to obtain the fusion optimal weight value of the heat amplitude value corresponding to each coordinate of each reconstructed thermal image, the linear transformation parameter a of the guided filtering is subjected to_kAnd b_kThe method for modeling the multi-objective optimization problem comprises the following specific steps:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_iP^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_iR^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_iP^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_iR^downAnd downsampling fused coarse weight map_iP^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_iR^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the gradient domain fine defect detail texture guide filtering cost function is obtained; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_iR^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is n belongs to I multiplied by J;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kMean value of the thermal amplitude, v, corresponding to the respective coordinate points in_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

the method comprises the steps of determining an optimal linear transformation coefficient for a local LoG operator space noise guide filtering cost function; epsilon is a regularization factor;

is a local LoG edge weight factor, which is definedThe following were used:

wherein LoG (. cndot.) is a Gaussian edge detection operator, I 'xJ' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_Lo_G0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_iR^downAnd downsampling the coarse weight map_iP^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a′_k) Preserving a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant dimensional and gradient changes, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing a multi-objective optimization related parameter, where the initialization iteration number g' is 0, and a set of uniformly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,...,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-target problems by utilizing a Chebyshev decomposition method:

step S2233, 1.., N for N_P: comparing and updating the speed, local optimum and global optimum solutions according to the particle swarm algorithm, using the vector corresponding to each weight

Direction vector of

Guiding the evolution direction of each population solution, and reserving a non-dominated guided filtering linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment: if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting a guide filter linear transformation coefficient compromise solution with the maximum weight membership degree from the AP, and recording an optimal weight vector group corresponding to the guide filter linear transformation coefficient compromise solution

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

Preferably, the step wherein a total of | C | typical type defect infrared reconstructed images of each detection area in two large size impact test pieces_Def.(1)R,…,_Def.(i)R,…,_Def.(C)R, down sampling each image to obtain a down sampled thermal image containing a lower amount of infrared thermal radiation data_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(C)R^downAnd (4) the size dimension of the down-sampled thermal image is M 'multiplied by N', and the following multi-target guiding filtering weight acquisition layer steps are executed based on the down-sampled thermal image:

step S21, based on the down-sampling infrared thermal image_Def.(i)R^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_Def.(i)P^down：

_Def.(i)H^down＝_Def.(i)R^down*L

_Def.(i)S^down＝|_Def.(i)H^down|*GF

Wherein L is a laplacian filter; non-viable cells_Def.(i)H^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in a typical type defect downsampling thermal image of the ith detection area based on the following formula_Def.(i)P^down：

Wherein the content of the first and second substances,

for downsampling coarse weight maps_Def.(i)P^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,

is composed of_Def.(i)P^downThe thermal amplitude value of the kth coordinate point of (1) is fused with the weight value,

is a heat amplitude significance characteristic diagram_Def.(i)S^downA radiation significance level value corresponding to the kth coordinate point, k being 1., M 'x N';

step S22, making a picture based on the downsampled thermal image_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(C)R^downGreat weight map of integration of } and downsampling_Def.(1)P^down,…,_Def.(i)P^down,…,_Def.(C)P^downPerforming multi-objective optimization guide filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: infrared sampling thermal image of typical type defect in ith detection area_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. down-sampling infra-red thermal images_Def.(i)R^downAt the kth coordinate point of_Def.(i)R_k ^downA centered local rectangular window, k is 1., M 'x N', which has a size of (2r +1) × (2r +1), the input/output relationship of the multi-target guided filtering is:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downTypical type defect downsampling output image of i-th detection area obtained by carrying out multi-target guide filtering on input image_Def.(i)O^downThe nth coordinate point of (a) corresponds to a pilot filter output value, N is 1.

Is composed of_Def.(i)R^downThe thermal amplitude value of the down-sampling reconstructed image corresponding to the nth coordinate point; a is_kAnd b_kIs shown in

Centered guided filter window w_kLinear transformation parameters within;

step S222, in order to obtain the fusion optimal weight value of the thermal amplitude value corresponding to each coordinate of each reconstructed thermal image of each typical defect type of each infrared detection area, the linear transformation parameter a of the guide filtering is subjected to_kAnd b_kModeling a multi-objective optimization problem:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_Def.(i)P^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_Def.(i)P^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_Def.(i)R^downAnd downsampling fused coarse weight map_Def.(i)P^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_Def.(i)R^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the gradient domain fine defect detail texture guide filtering cost function is obtained; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is N belongs to M 'multiplied by N';

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kMean value of the thermal amplitude, v, corresponding to the respective coordinate points in_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

for optimal linear transformation coefficients determined by local LoG operator space noise-oriented filtering cost function(ii) a Epsilon is a regularization factor;

is a local LoG edge weight factor, which is defined as follows:

wherein LoG (. cndot.) is a Gaussian edge detection operator, M 'xN' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_LoG0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_Def.(i)R^downAnd downsampling the coarse weight map_Def.(i)P^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a_k') remaining a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant size and gradient variation, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing multi-objective optimization related parameters; the number of initialization iterations g' is 0, and a set of evenly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,...,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-target problems by utilizing a Chebyshev decomposition method

Step S2233, 1.., N for N_PComparing and updating speed, local optimal solution and global optimal solution according to a particle swarm algorithm, and reserving a non-dominated guided filter linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment, if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting a guide filter linear transformation coefficient compromise solution with the maximum weight membership degree from the AP, and recording the corresponding optimal weight vector

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

Preferably, wherein the multi-objective-based optimal weight matching parameters

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, decomposing each original infrared reconstruction thermal image except the background area into a base layer infrared thermal image₁B,...,_iB,...,_{|Num_Cluster|}B } and a detailLayer infrared thermal image₁D,...,_iD,...,_{|Num_Cluster|}D }; reconstruction of thermal images from ith defect region_iR is, for example, i ═ 1., | Num _ Cluster | -1, which is obtained by using the following formula_iBase layer infrared thermal image of R_iB and detail layer infrared thermal image_iD：

_iB＝_iR*Z

_iD＝_iR-_iB

Wherein Z is an average filter;

step S32, obtaining a coarse weight map on the original infrared reconstruction thermal image layer based on the following formula_iP：

_iH＝_i R*L

_iS＝|_iH|*GF

Wherein L is Laplace filter, GF is a Gaussian low-pass filter, and the thermal amplitude fusion coarse weight map is obtained based on the following formula_iP：

Wherein the leaf_iP₁,...,_iP_k,...,_iP_I×JIs a coarse weight map_iThe thermal amplitude values of the respective position coordinates of P fuse the weight values,_iP_k(k is 1, …, I.times.J.) is_iThe thermal amplitude value of the kth coordinate point of P fuses the weight values,_iS_k(k-1, …, I × J) is a heat amplitude significance map_iThe radiation significance level value corresponding to the kth coordinate point in the S;

step S33 based on

Multi-target guiding filtering optimal filter operator MOGF for obtaining original infrared reconstruction thermal image layer_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

in order to ensure the consistency of the linear transformation coefficient in different guide filtering windows, the linear transformation coefficient a is used_kAnd b_kThe following modifications were made:

wherein, | w_nI is the number of coordinate points in the guide filter window with the nth coordinate as the center and is based on the linear transformation coefficient a_kAnd b_kThe expression of the final multi-target guiding filter operator is obtained as follows:

wherein the content of the first and second substances,_iO_nfor the thermal amplitude corresponding to the nth coordinate point in the output image of the multi-target oriented filtering, the operation of filtering by using the obtained multi-target optimal linear transformation coefficient to obtain a multi-target oriented filtering operator is recorded as MOGF_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining the optimal guiding filter operator MOGF by utilizing multi-objective optimization_r,ε(P, R) performing multi-target guiding filtering on the obtained thermal amplitude fusion coarse weight graph on the original thermal image layer to obtain a corrected thermal amplitude fusion weight image of the base layer and the detail layer:

wherein_iW^BAnd_iW^Dfusing an ith basic layer heat amplitude fusion fine modification weight value graph and an ith detail layer heat radiation value fusion fine modification weight value graph which are subjected to multi-target guide filtering for fusing a coarse weight graph，_iP is the ith fusion weight map of thermal radiation values,_ir is the ith reconstructed thermal image, R₁,ε₁,r₂,ε₂Respectively, the parameters of the corresponding guide filter; finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, map based on the obtained refined detail layer thermal amplitude fusion weight₁W^D,₂W^D,…,_{|Num_Cluster|- 1}W^DMap for integrating weights of heat amplitude of foundation layer₁W^B,₂W^B,…,_{|Num_Cluster|-1}W^BAnd (3) fusing the detail layer thermal image information and the base layer thermal image information among the thermal reconstruction images of different defect areas except the background area to obtain a base layer thermal image and a detail layer thermal image fused with a plurality of pieces of reconstruction thermal image effective information:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, a multi-target oriented filtering fusion image which is fused with a plurality of pieces of reconstructed thermal image defect effective information and simultaneously considers the retention requirement of large-size defects, the retention requirement of detail textures of micro defects and the retention requirement of integral noise elimination in each thermal image is obtained; inputting the high-quality infrared reconstruction fusion image F fused with the characteristics of various complex defects into the infrared thermal image segmentation and defect quantitative analysis steps so as to further extract the quantitative characteristic information of various defects.

Preferably, the third step is based on the multi-target optimal weight proportioning parameter

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, a total | C | typical type defect infrared reconstruction image of each detection area in large-size impact test piece_Def.(1)R,...,_Def.(i)R,...,_Def.(|C|)Each of R is decomposed into a base layer infrared thermal image { inf.base [ Def. (1)],...,Inf.Base[Def.(i)],...,Inf.Base[Def.(|C|)]And a detailed layer infrared thermal image { inf],...,Inf.Detail[Def.(i)],...,Inf.Detail[Def.(|C|)]}; reconstruction of thermal images of defects of type typical of the ith inspection area_Def.(i)R is obtained by the following formula_Def.(1)Base infrared thermal image of typical type defect base layer and detail layer of R [ Def. (i)]And inf]：

Inf.Base[Def.(i)]＝_Def.(i)R*Z

Inf.Detail[Def.(i)]＝_Def.(i)R-Inf.Base[Def.(i)]

Wherein Z is an average filter, i ═ 1., | C |;

step S32, obtaining an initial heat radiation coarse fusion weight map based on the following formula:

_Def.(i)H＝_Def.(i)R*L

_Def.(i)S＝|_Def.(i)H|*GF

wherein L is Laplace filter, GF is a Gaussian low-pass filter, and the thermal amplitude fusion coarse weight map is obtained based on the following formula_Def.(i)P：

Wherein the leaf_Def.(i)P₁,…,_Def.(i)P_k,…,_Def.(i)P_M×NIs a coarse weight map_Def.(i)The thermal amplitude values of the respective position coordinates of P fuse the weight values,_Def.(i)P_kis composed of_Def.(i)The thermal amplitude value of the kth coordinate point of P fuses the weight values,_Def.(i)S_kis a heat amplitude significance characteristic diagram_Def.(i)A radiation significance level value corresponding to a kth coordinate point in the S, wherein k is 1.

Step S33 based on

Multi-target guiding filtering optimal filter operator MOGF for obtaining original infrared reconstruction thermal image layer_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

in order to ensure the consistency of the linear transformation coefficient in different guide filtering windows, the linear transformation coefficient a is used_kAnd b_kThe following modifications were made:

wherein, | w_nAnd l is the number of coordinate points in the guide filtering window with the nth coordinate as the center, and the expression of the final multi-target guide filtering operator is as follows:

wherein the content of the first and second substances,_Def.(i)R_nfusing and refining weight values for the thermal amplitude values corresponding to the nth coordinate point in the output image of the multi-target guiding filtering; the operation of filtering the weight graph of the infrared reconstruction thermal image of the ith infrared detection area by using the obtained multi-target optimal linear transformation coefficient through a multi-target guiding filtering operator is recorded as

Wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining optimal guiding filter operator by utilizing multi-objective optimization

And performing multi-target guiding filtering on the thermal amplitude fusion coarse weight graph of the obtained infrared reconstruction thermal image of the ith infrared detection area to obtain a corrected thermal amplitude fusion weight image of the base layer and the detail layer:

wherein WM.Base [ Def. (i)]And wm]Fusing a basic layer thermal amplitude fusion refinement weight value graph of an ith infrared detection area typical type defect infrared reconstruction thermal image after fusing a coarse weight graph and performing multi-target guiding filtering and a detailed layer thermal radiation value fusion refinement weight value graph of the ith infrared detection area infrared reconstruction thermal image,_Def.(i)p is a heat radiation value fusion coarse weight map of the infrared reconstruction thermal image of the ith infrared detection area,_Def.(i)r is the infrared reconstructed thermal image of the ith infrared detection area, R₁,ε₁,r₂,ε₂Respectively corresponding parameters of the guide filter, and finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, based on the obtained fine-modified infrared thermal amplitude fusion weight map of the detail layer of the typical type defect in each infrared detection area { wm.detail [ Def. (1) ], wm.detail [ Def. (i) ], wm.detail [ Def. (| C |) ] and the infrared thermal amplitude fusion weight map of the base layer { wm.base [ Def. (1) ], wm.base [ Def., (i) ], wm.base [ Def., (| wm.base |) ], and the thermal image information of the base layer and the thermal image information of the detail layer between the thermal reconstruction images of the typical type defects in different detection times in the large-size test piece are fused, so as to obtain the thermal image of the base layer and the thermal image of the detail layer fused with the effective information of the multiple detection areas:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, the infrared detection fusion thermal image fusing the effective information of the reconstruction thermal image defects of the typical type defects of a plurality of infrared detection areas of the large-size test piece is obtained, the infrared fusion thermal image integrates the excellent characteristics of a plurality of guide filters by utilizing a multi-objective optimization algorithm, multiple times of infrared detection are carried out, the typical type defects of different areas are fused together, the high-quality simultaneous imaging of the defects of the large-size pressure container is realized, and the high-quality infrared reconstruction fusion image F simultaneously fusing the typical characteristics of the defects of the plurality of detection areas is input into the infrared thermal image segmentation and defect quantitative analysis steps, so that the quantitative characteristic information of various defects is further extracted.

The invention at least comprises the following beneficial effects:

1. the invention discloses an infrared thermal image fusion large-size pressure container crack defect feature extraction method based on double-layer multi-target optimization and guided filtering, which is characterized in that a transient thermal response set is rapidly and adaptively clustered through a DPC (design rule base) clustering algorithm or a BIRCH (binary-coded redundancy algorithm) clustering algorithm based on density peak values, so that various typical feature thermal responses corresponding to various defects in different infrared detection areas of a large-size pressure container are obtained from different thermal image sequences, thermal image reconstruction is carried out, and visual imaging of typical type defects in the current infrared detection area is realized. Obtaining the respective reconstructed thermal images of the various defectsAnd then, effective information in the reconstructed thermal images of different types of defects is combined by using an image fusion algorithm combined with a double-layer multi-objective evolution optimization algorithm and a guided filtering algorithm, so that the detection capability and the defect characteristic characterization performance of a single infrared thermal image are improved. And after downsampling the original reconstruction thermal image, inputting the downsampled original reconstruction thermal image into a multi-target oriented filtering optimal weight parameter acquisition layer, and obtaining a Pareto optimal non-dominated solution set based on a multi-target evolution optimization algorithm so as to obtain the multi-target oriented filtering optimal weight ratio. And then returning the optimal weight ratio parameter to the original infrared thermal image fusion layer, and combining the specific excellent performances of various guide filters by using the optimal weight parameter, thereby absorbing the advantages of various guide filters and constructing a multi-target optimal guide filter operator MOGF_r,ε(P, R). After the original infrared reconstruction thermal image is subjected to image decomposition to obtain a base layer image and a detail layer image of the thermal image, based on a multi-target optimal oriented filter operator MOGF_r,ε(P, R) obtaining different refinement fusion weight maps on two scales of a base layer and a detail layer. And respectively guiding the weighted fusion between the images of the base layers and the weighted fusion between the images of the detail layers based on the corrected weight maps. And finally, combining the detail layer image and the basic layer image after weighted average to obtain a final fusion image. Performing defect segmentation positioning and quantification operation based on the final fusion thermal image;

2. the invention combines DPC clustering algorithm and BIRCH clustering algorithm based on density peak value to realize high-efficiency rapid and self-adaptive clustering of transient thermal response information, and improves clustering efficiency, thereby further reducing the overall detection time of the detection algorithm;

3. the invention adopts an image fusion strategy, and can fuse effective information of a plurality of reconstructed thermal images. The detection performance of a single thermal image is improved, and the problem that the single-detected image defect of the complicated type test piece defect caused by ultra-high speed impact is incomplete due to the limitation of infrared detection performance can be solved by carrying out image fusion on a plurality of thermal images;

4. the invention adopts an image fusion strategy combining double-layer multi-objective optimization and guided filtering. Based on the weight acquisition layer and the original thermal image fusion layer, the optimal weighting proportioning parameters of various guide filters can be acquired more quickly, so that the advantages of the various guide filters are combined together, and the performance of the fusion image on complex defect contour edges and fine size defects is further improved while the image noise is smoothed.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.

Drawings

FIG. 1 is a flowchart of an embodiment of a method for extracting infrared thermal image fusion defect features based on double-layer multi-objective optimization and guided filtering in example 1;

FIG. 2 is a flow diagram of the overall fusion framework of example 1 based on the fusion of multiple (two for example) infrared thermal images in combination with multiobjective optimization and guided filtering;

FIG. 3 is a flowchart of embodiment 1, in which multi-objective optimization and guided filtering are specifically combined to obtain a modified weighted image of each image layer;

FIG. 4 is a graph of the results of example 1 using DPC clustering algorithm based on density peaks to classify the transient thermal response set in the thermal image sequence of the first detection zone;

FIG. 5 is a graph of the results of example 1 using DPC clustering algorithm based on density peaks to classify the transient thermal response set in the thermal image sequence of the second detection zone

FIG. 6 is a graph of typical characteristic transient thermal response of a defect of the type typical of the first inspection area extracted in example 1;

FIG. 7 is a graph of typical characteristic transient thermal response of a defect of the type typical of the second inspection area extracted in example 1;

FIG. 8 is an infrared reconstructed thermal image obtained based on a transient thermal response representative of a type of defect in a first inspection area according to example 1;

FIG. 9 is an infrared reconstructed thermal image obtained based on a transient thermal response representative of a defect of the type representative of the second inspected area of example 1;

FIG. 10 is a block diagram of an optimal leading edge of infrared thermal image fusion parameters based on multi-objective optimization in combination with multiple guided filters and an optimal thermal image fusion parameter solution based on weighted membership in example 1;

FIG. 11 is a graph a of the fusion weights of the refined base-layer image of the original-scale thermal image based on the modified optimal multi-objective guided filtering fusion operator in example 1;

FIG. 12 is a graph b of the fusion weights of the refined base-layer image of the original-scale thermal image based on the modified optimal multi-objective guided filtering fusion operator in example 1;

FIG. 13 is a graph c of the original-scale thermal image refinement detail layer image fusion weights corrected based on the obtained optimal multi-objective guided filtering fusion operator in example 1;

FIG. 14 is a graph d of the original-scale thermal image refinement detail layer image fusion weights corrected based on the obtained optimal multi-objective guided filtering fusion operator in example 1;

FIG. 15 is the resulting infrared fusion thermal image based on two-layer multiobjective optimization and guided filtering of example 1;

description of the invention

FIG. 16 is a flowchart of a multi-type damage detection image feature comprehensive interpretation method according to the embodiment 2 of the present invention;

FIG. 17 is a flowchart of an overall fusion framework based on multi-sheet (two for example) infrared thermal image fusion combining multiobjective optimization and guided filtering of example 2;

FIG. 18 is a flowchart of obtaining a modified weighted image for each image layer by a specific combination of two-layer multi-objective optimization and guided filtering in example 2;

FIG. 19 is a graph showing the results of embodiment 2 after classifying the transient thermal response sets by using the BIRCH clustering algorithm;

FIG. 20 is a graph of a typical characteristic transient thermal response of a background region extracted in example 2;

FIG. 21 is a graph of a typical characteristic transient thermal response of a first type of defect region extracted in example 2;

FIG. 22 is a graph of typical characteristic transient thermal response of a second type of defect region extracted in example 2;

FIG. 23 is an infrared reconstructed thermal image of a non-defect background area obtained based on a typical characteristic transient thermal response of the background area in example 2;

FIG. 24 is an infrared reconstructed thermal image of a central impact pit area obtained according to example 2 based on a typical characteristic transient thermal response curve of a first type of defect area;

FIG. 25 is an infrared reconstructed thermal image of an edge fine impact sputter damage region obtained based on a typical characteristic transient thermal response curve of a second type of defect region in example 2;

FIG. 26 is a block diagram of an optimal thermal image fusion parameter solution based on the optimal leading edge of the infrared thermal image fusion parameters obtained by multi-objective optimization in combination with a plurality of steering filters and based on weighted membership in example 2;

FIG. 27 is a graph e of the fusion weights of the refined base-layer images of the original-scale thermal image based on the modified optimal multi-objective guided filtering fusion operator in example 2;

FIG. 28 is a graph f of the fusion weights of the refined base-layer images of the original-scale thermal image based on the modified optimal multi-objective guided filtering fusion operator in example 2;

FIG. 29 is a graph g of the original-scale thermal image refinement detail layer image fusion weights corrected based on the obtained optimal multi-objective guided filtering fusion operator in example 2;

FIG. 30 is a graph of the original-scale thermal image refinement detail layer image fusion weights h modified in accordance with the obtained optimal multi-objective guided filtering fusion operator in example 2;

FIG. 31 is the resulting infrared fusion thermal image based on multiobjective optimization and guided filtering of example 2.

Detailed Description

The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.

Example 1

As shown in fig. 1-3: the invention relates to a comprehensive interpretation method for infrared detection characteristics of a large-size test piece, which comprises the following steps of:

the method comprises the following steps of firstly, carrying out infrared detection on a large-size test piece for multiple times to obtain multiple thermal image sequences of the large-size test piece, and obtaining multiple reconstructed infrared thermal images of the large-size test piece from the multiple thermal image sequences by utilizing an infrared feature extraction and infrared thermal image reconstruction algorithm, wherein the specific method comprises the following steps:

step S11, using a three-dimensional matrix set { S } for a plurality of thermal image sequences acquired from a thermal infrared imager¹,...,Sⁱ,...,S^|C|Denotes wherein SⁱRepresenting a thermal image sequence obtained by an infrared thermal imager in the ith infrared detection, and | C | representing the total number of the thermal image sequences; sⁱ(M, N, T) represents a temperature value at the coordinate position of the mth row and the nth column of the tth frame thermal image in the ith thermal image sequence, wherein T is 1, the.

Step S12, for the ith thermal image sequence SⁱExtracting the ith thermal image sequence S by utilizing a transient thermal response data extraction algorithm based on block variable step lengthⁱTransient thermal response data set X of mesovalueⁱ(g) (ii) a Passing the ith thermal image sequence S through a thresholdⁱDecomposition into K different data blocks_kSⁱ(m ', n', t) wherein k represents the ith thermal image sequence SⁱM ', n', t respectively represent temperature values at the coordinate positions of the m 'th row, the n' th column and the t-th frame of the kth sub-data block, and then define the ith thermal image sequence S according to the temperature change characteristics in different data blocksⁱStep size of search line in k-th data block_kRSSⁱAnd column step size_kCSSⁱ(ii) a Based on different search steps, K1, K, in different data blocks, comparing correlation coefficients between data points, and searching for a series of correlation coefficients greater than a threshold THC_crAnd adding the ith thermal image sequence SⁱTransient thermal response data set X in (1)ⁱ(g)；

Step S13, using DPC clustering algorithm based on density peak to classify the ith thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) InAdaptive clustering of transient thermal response; firstly, randomly calculating the distance between two transient thermal response samples; calculating the local density rho of each transient thermal response sample according to the truncation distance_i(ii) a Calculating the distance delta of each transient thermal response sample to the transient thermal response sample point which has larger local density and is closest to the transient thermal response sample point_i(ii) a Using p_iAnd delta_iDrawing a decision graph and dividing rho_iAnd delta_iThe points that are all relatively high are marked as cluster centers, p_iRelatively low but delta_iRelatively high points are marked as noise; distributing the rest transient thermal response sample points to the nearest neighbor cluster of the sample points with the density larger than that of the sample points to obtain the final transient thermal response cluster division, and dividing the thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) Adaptive clustering to form a set of clusters

Wherein H represents a defect type label, and H represents the total number of types of complex defects existing in the current infrared detection area;

step S14, respectively extracting representative characteristic transient thermal responses of various complex defects in the ith detection area from different clusters and reconstructing thermal images based on the transient thermal responses; calculating the clustering center of each category in the clustering result as the representative characteristic transient thermal response of each category of defects:

wherein

For the h-th clustering result_X(g)Cluster[h]H-1, …, the kth transient thermal response in H_X(g)Cluster[h]L is the total number of transient thermal responses contained in the h-th clustering result, and a matrix Y is formed by the representative transient thermal responses of all the types of defectsⁱ；

Using matrix YⁱAnd SⁱPerforming infrared thermal image reconstruction on the information, and performing the ith thermal image sequenceColumn SⁱEach frame image is extracted into a column vector according to columns and arranged in time sequence to form a two-dimensional image matrix O of M multiplied by N rows and T columnsⁱObtaining a heat amplitude value reconstruction matrix R of the ith detection based on the following transformation formulaⁱ：

Wherein the content of the first and second substances,

is H × T matrix, and is a representative transient thermal response matrix YⁱPseudo-inverse matrix of (O)ⁱ)^TIs a two-dimensional image matrix OⁱTranspose matrix, obtaining reconstruction matrix of H rows and M multiplied by N columns, intercepting reconstruction matrix RⁱForming an M multiplied by N two-dimensional image for each line to obtain H M multiplied by N two-dimensional images, namely reconstructing thermal images containing different thermal response area characteristic information in the thermal image sequence obtained by the ith infrared detection, and recording the non-defect background area reconstruction thermal images as_BR, recording the reconstructed thermal image corresponding to each type of defect area as_hR, H1, wherein each reconstructed thermal image contains, in addition to the thermal image of the background area free of defect lesions, the characteristic thermal reconstruction information of one type of defect among the complex types of defects currently detected, and the reconstructed thermal image of the type of defect in the detected area obtained in the ith infrared detection is recorded as the reconstructed thermal image of the type of defect in the detected area_Def.(i)R；

And step S15, if i < | C |, i +1 and the steps S12 to S14 are repeated until all the types of defect reconstruction thermal images in the current detected area are respectively obtained from a plurality of thermal image sequences obtained by multiple detections. Then calculating PSNR values of reconstructed thermal images of all types of defects in the current area, and selecting reconstructed thermal images of typical types of defects in each detection area based on the maximum principle of peak signal-to-noise ratio (PSNR), namely obtaining a reconstructed thermal image set of typical types of defects in each detection area of a large-size test piece_Def.(1)R,…,_Def.(i)R,…,_Def.(C)R }, wherein_Def.(i)R represents a typical type of defect reconstruction thermal image of the detected region in the ith thermal image sequence, i 1.

Step two, carrying out infrared reconstruction on a total | C | typical type defect infrared reconstruction image of each detection area in the large-size impact test piece_Def.(1)R,…,_Def.(i)R,…,_Def.(C)R, down sampling each image to obtain a down sampled thermal image containing a lower amount of infrared thermal radiation data_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(C)R^downAnd (4) the size dimension of the down-sampled thermal image is M 'multiplied by N', and the following multi-target guiding filtering weight acquisition layer steps are executed based on the down-sampled thermal image:

step S21, based on the down-sampling infrared thermal image_Def.(i)R^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_Def.(i)P^down：

_Def.(i)H^down＝_Def.(i)R^down*L

_Def.(i)S^down＝|_Def.(i)H^down|*GF

Wherein L is a laplacian filter; non-viable cells_Def.(i)H^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in a typical type defect downsampling thermal image of the ith detection area based on the following formula_Def.(i)P^down：

Wherein the content of the first and second substances,

for down-sampling coarseWeight graph_Def.(i)P^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,

is composed of_Def.(i)P^downThe thermal amplitude value of the kth coordinate point of (1) is fused with the weight value,

is a heat amplitude significance characteristic diagram_Def.(i)S^downA radiation significance level value corresponding to the kth coordinate point, k being 1., M 'x N';

step S22, making a picture based on the downsampled thermal image_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(C)R^downGreat weight map of integration of } and downsampling_Def.(1)P^down,…,_Def.(i)P^down,…,_Def.(C)P^downPerforming multi-objective optimization guide filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: infrared sampling thermal image of typical type defect in ith detection area_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. down-sampling infra-red thermal images_Def.(i)R^downAt the kth coordinate point of

A centered local rectangular window, k is 1., M 'x N', which has a size of (2r +1) × (2r +1), the input/output relationship of the multi-target guided filtering is:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downTypical type defect downsampling output image of i-th detection area obtained by carrying out multi-target guide filtering on input image_Def.(i)O^downThe guide filtering output value corresponding to the nth coordinate point;

is composed of_Def.(i)R^downThe thermal amplitude value of the down-sampling reconstructed image corresponding to the nth coordinate point; a is_kAnd b_kIs shown in

Centered guided filter window w_kLinear transformation parameters within;

step S222, in order to obtain the fusion optimal weight value of the thermal amplitude value corresponding to each coordinate of each reconstructed thermal image of each typical defect type of each infrared detection area, the linear transformation parameter a of the guide filtering is subjected to_kAnd b_kModeling a multi-objective optimization problem:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_Def.(i)P^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_Def.(i)P^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_Def.(i)R^downAnd downsampling fused coarse weight map_Def.(i)P^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_Def.(i)R^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

to be formed by a gradientThe optimal linear transformation coefficient is determined by the domain fine defect detail texture guide filtering cost function; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is N belongs to M 'multiplied by N';

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kMean value of the thermal amplitude, v, corresponding to the respective coordinate points in_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

the method comprises the steps of determining an optimal linear transformation coefficient for a local LoG operator space noise guide filtering cost function; epsilon is a regularization factor;

is a local LoG edge weight factor, which is defined as follows:

wherein LoG (. cndot.) is a Gaussian edge detection operator, M 'xN' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_LoG0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_Def.(i)R^downAnd downsampling the coarse weight map_Def.(i)P^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a_k') remaining a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant size and gradient variation, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing multi-objective optimization related parameters; the number of initialization iterations g' is 0, and a set of evenly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,…,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-target problems by utilizing a Chebyshev decomposition method

Step S2233, 1.., N for N_PComparing and updating the speed, the local optimum and the global optimum according to the particle swarm algorithm, using the vector corresponding to each weight

Direction vector of

Guiding the evolution direction of each population solution, and reserving a non-dominated guided filtering linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment, if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting guiding filtering linear transformation with maximum weighting membership degree from APThe coefficient compromise solution records the corresponding optimal weight vector set

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

Step three, based on the optimal weight ratio parameter of the multiple objectives

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, a total | C | typical type defect infrared reconstruction image of each detection area in large-size impact test piece_Def.(1)R,...,_Def.(i)R,...,_Def.(|C|)Each of R is decomposed into a base layer infrared thermal image { inf.base [ Def. (1)],...,Inf.Base[Def.(i)],...,Inf.Base[Def.(|C|)]And a detailed layer infrared thermal image { inf],...,Inf.Detail[Def.(i)],...,Inf.Detail[Def.(|C|)]}; reconstruction of thermal images of defects of type typical of the ith inspection area_Def.(i)R is obtained by the following formula_Def.(1)Base infrared thermal image of typical type defect base layer and detail layer of R [ Def. (i)]And inf]：

Inf.Base[Def.(i)]＝_Def.(i)R*Z

Inf.Detail[Def.(i)]＝_Def.(i)R-Inf.Base[Def.(i)]

Wherein Z is an average filter, i ═ 1., | C |;

step S32, obtaining an initial thermal radiation coarse fusion weight chart based on the following formula

_Def.(i)H＝_Def.(i)R*L

_Def.(i)S＝|_Def.(i)H|*GF

Wherein L is Laplace filter, GF is a Gaussian low-pass filter, and the thermal amplitude fusion coarse weight map is obtained based on the following formula_Def.(i)P：

Wherein the leaf_Def.(i)P₁,…,_Def.(i)P_k,…,_Def.(i)P_M×NIs a coarse weight map_Def.(i)The thermal amplitude values of the respective position coordinates of P fuse the weight values,_Def.(i)P_kis composed of_Def.(i)The thermal amplitude value of the kth coordinate point of P fuses the weight values,_Def.(i)S_kis a heat amplitude significance characteristic diagram_Def.(i)A radiation significance level value corresponding to a kth coordinate point in the S, wherein k is 1.

Step S33 based on

Multi-target guiding filtering optimal filter operator MOGF for obtaining original infrared reconstruction thermal image layer_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

in order to ensure the consistency of the linear transformation coefficient in different guide filtering windows, the linear transformation coefficient a is used_kAnd b_kThe following modifications were made:

wherein, | w_nAnd l is the number of coordinate points in the guide filtering window with the nth coordinate as the center, and the expression of the final multi-target guide filtering operator is as follows:

wherein the content of the first and second substances,_Def.(i)R_nfusing and refining weight values for the thermal amplitude values corresponding to the nth coordinate point in the output image of the multi-target guiding filtering; performing multi-target guiding on the weight map of the infrared reconstruction thermal image of the ith infrared detection area by using the obtained multi-target optimal linear transformation coefficientThe operation of the filter operator for filtering is recorded as

Wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining optimal guiding filter operator by utilizing multi-objective optimization

And performing multi-target guiding filtering on the thermal amplitude fusion coarse weight graph of the obtained infrared reconstruction thermal image of the ith infrared detection area to obtain a corrected thermal amplitude fusion weight image of the base layer and the detail layer:

wherein WM.Base [ Def. (i)]And wm]Fusing a basic layer thermal amplitude fusion refinement weight value graph of an ith infrared detection area typical type defect infrared reconstruction thermal image after fusing a coarse weight graph and performing multi-target guiding filtering and a detailed layer thermal radiation value fusion refinement weight value graph of the ith infrared detection area infrared reconstruction thermal image,_Def.(i)p is a heat radiation value fusion coarse weight map of the infrared reconstruction thermal image of the ith infrared detection area,_Def.(i)r is the infrared reconstructed thermal image of the ith infrared detection area, R₁,ε₁,r₂,ε₂Respectively corresponding parameters of the guide filter, and finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, based on the obtained fine-modified infrared thermal amplitude fusion weight map of the detail layer of the typical type defect in each infrared detection area { wm.detail [ Def. (1) ], wm.detail [ Def. (i) ], wm.detail [ Def. (| C |) ] and the infrared thermal amplitude fusion weight map of the base layer { wm.base [ Def. (1) ], wm.base [ Def., (i) ], wm.base [ Def., (| wm.base |) ], and the thermal image information of the base layer and the thermal image information of the detail layer between the thermal reconstruction images of the typical type defects in different detection times in the large-size test piece are fused, so as to obtain the thermal image of the base layer and the thermal image of the detail layer fused with the effective information of the multiple detection areas:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, the infrared detection fusion thermal image fusing the effective information of the reconstruction thermal image defects of the typical type defects of a plurality of infrared detection areas of the large-size test piece is obtained, the infrared fusion thermal image integrates the excellent characteristics of a plurality of guide filters by utilizing a multi-objective optimization algorithm, multiple times of infrared detection are carried out, the typical type defects of different areas are fused together, the high-quality simultaneous imaging of the defects of the large-size pressure container is realized, and the high-quality infrared reconstruction fusion image F simultaneously fusing the typical characteristics of the defects of the plurality of detection areas is input into the infrared thermal image segmentation and defect quantitative analysis steps, so that the quantitative characteristic information of various defects is further extracted.

In this embodiment, two areas of defect on the test piece need to be detected, namely a first area of artificially surface cored defect 1 and a second area of artificially filled defect 2.

A flow chart of an overall fusion framework based on fusion of multiple (two for example) infrared thermal images in combination with multiobjective optimization and guided filtering is shown in fig. 2.

A flowchart of obtaining the modified weighted image of each image layer by specifically combining the multi-objective optimization and the guided filtering is shown in fig. 3.

In this example, the result of classifying the transient thermal response set of the first detection region by DPC clustering algorithm based on density peaks is shown in fig. 4, and the result of classifying the transient thermal response set of the second detection region is shown in fig. 5.

Obtaining a clustering center corresponding to each transient thermal response set after a DPC clustering algorithm based on density peak value, and using the clustering center as a typical characteristic transient thermal response of typical type defects of each region_Def.(1)R and_Def.(2)and R is shown in the specification. Their respective typical characteristic transient thermal response curves are shown in fig. 6 and 7.

After typical characteristic transient thermal response curves of typical type defects of all areas of the test piece are obtained, an infrared thermal image reconstruction algorithm is carried out on the obtained typical characteristic transient thermal response curves, and the artificial surface hole digging of the first area of the material is obtained_Def.(1)R corresponding reconstructed thermal image and second area artificially filled defect_Def.(2)The reconstructed thermal images corresponding to R are shown in fig. 8 and 9, and their respective highlighted defect types are shown in the figure.

By using the method for solving the linear transformation parameters of the optimal guided filtering by combining multi-objective optimization and guided filtering, a series of Pareto optimal non-dominated solutions are obtained, a Pareto optimal front-edge (PF) is obtained based on the Pareto optimal non-dominated solutions, and an optimal guided filtering thermal image fusion parameter solution is selected based on an optimal weighting membership principle, as shown in FIG. 10.

And obtaining an optimal guided filtering thermal image fusion parameter based on multi-target optimization and guided filtering to obtain a multi-target guided filtering optimal operator, and performing multi-target guided filtering operation on the weighted images corresponding to the base layer image and the detail layer image obtained after infrared reconstruction thermal image decomposition. And obtaining a refined weight map on each image level after multi-target guiding filtering correction. With W₁ ^BRepresenting the refined base layer weight map a,

represents the refined base layer weight graph b, W₁ ^DA refined detail level weight graph c is shown,

the refined base layer weight maps d are shown in fig. 11, 12, 13, and 14, respectively.

And performing infrared thermal image fusion operation on each layer of weight image corrected by the multi-target optimal guiding filtering operator to obtain infrared fusion thermal images of each region of the large-size pressure container as shown in fig. 15. The damage condition characteristics of the defects 1 and 2 can be clearly and simultaneously represented in the graph with high quality, and subsequent image segmentation and defect identification quantitative operation can be better carried out.

In the present embodiment, the extracted features that blend defects of a large-sized pressure vessel are shown in fig. 15.

It can be seen that the finally fused infrared detection image obtained by the embodiment has better detectability for defects of each area of the large-size pressure container.

Example 2

The method comprises the following steps of firstly, acquiring an infrared reconstruction thermal image from an infrared thermal image sequence by utilizing an infrared feature extraction and infrared thermal image reconstruction algorithm, and specifically comprises the following steps:

s11, S11, extracting characteristic information of each transient thermal response from a thermal image sequence S acquired by a thermal infrared imager and forming a characteristic matrix Fe; wherein S (I, J, T) represents pixel values of an ith row and a jth column of a T-frame thermal image of the thermal image sequence, T is 1.. T, T is a total frame number, I is 1.. T, I is a total row number, and J is 1.. J, J is a total column number; fe (i, j, f) represents the f (f is 1, …,6) th feature information corresponding to the coordinate position of the i-th row and the j-th column of the feature matrix; the first characteristic information is a thermal amplitude peak value, namely Fe (i, j,1) is max (S (i, j, and): S (i, j, and) represents the temperature change condition of the ith row and the jth column in the whole T frame process; the second characteristic information being the mean value of the thermal amplitudes, i.e.

The 3 rd feature information is coefficient of variation, i.e.

The 4 th characteristic information being the rate of rise, i.e.

Wherein t is_maxRepresenting the frame number corresponding to the heat radiation peak value; the 5 th characteristic information being the rate of descent, i.e.

The 6 th characteristic information is the heat radiation kurtosis and is used for representing the peak or the flatness of the transient thermal response curve, namely

Finally obtaining a characteristic matrix Fe of the thermal image sequence S;

step S12, self-adaptively clustering the feature matrix Fe into | C | classes by using a BIRCH clustering algorithm; constructing a triple clustering characteristic CF, wherein CF is { N, LS, SS }, wherein N is the number of sample points owned by the node, LS is a sum vector of characteristic dimensions of the sample points owned by the node, and SS represents a square sum of the characteristic dimensions of the sample points owned by the node; generating a clustering feature tree based on the CFs, defining the maximum CF number B of the internal nodes, the maximum CF number L of the leaf nodes, and the maximum sample radius threshold T of each CF of the leaf nodes; continuously searching for clustering characteristics meeting the requirement within a hypersphere radius threshold T from a root node, creating a new leaf node under the condition that the number of leaf nodes is less than L, and putting a new sample meeting the condition; judging, checking and splitting leaf nodes to finally obtain a clustering feature tree; screening the clustering feature tree to remove abnormal CF nodes; using a global clustering algorithm to perform clustering repair on all leaf nodes to obtain a clustering feature tree; taking the global clustering center point as a seed, redistributing the data points to the nearest seed, ensuring that repeated data are distributed into the same cluster, and adding a cluster label; synchronizing the clustering Cluster labels to each transient thermal response in the original thermal image sequence to form a Cluster [ h ], wherein h is 1,2,., | Num _ Cluster | represents a category label, and | Num _ Cluster | represents the total number of categories;

step S13, respectively extracting typical characteristic transient thermal responses from different clusters and reconstructing a thermal image based on the typical characteristic transient thermal responses; calculating the clustering center of each category in the clustering result as the typical characteristic transient thermal response of each category of defects:

wherein

Cluster result h for h]The kth of | Num _ Cluster | represents the transient thermal response, Cluster [ h ═ 1]Forming a matrix Y by typical transient thermal responses of various types of defects for the total number of transient thermal responses contained in the h-th clustering result;

the infrared thermal image reconstruction is carried out by utilizing the information of the matrixes Y and S, each frame image of S is extracted into a column vector according to columns and is arranged in time sequence to form an I multiplied by J row and T column two-dimensional image matrix O, and a reconstruction matrix R is obtained based on the following transformation formula:

wherein the content of the first and second substances,

is a matrix of | Num _ Cluster | xT, which is a pseudo-inverse of the matrix Y, O^TThe method is characterized in that the method is a transpose matrix of a two-dimensional image matrix O, and an obtained reconstruction matrix R is | Num _ Cluster | rows and I multiplied by J columns; intercepting each row of the reconstruction matrix R to form an I multiplied by J two-dimensional image to obtain a Num _ Cluster I multiplied by J two-dimensional image, wherein the images are infrared reconstruction thermal images containing different thermal response area characteristic information, and recording a non-defect background area reconstruction thermal image in the images as a_BR, reconstructing heat map corresponding to transient thermal response of all class characteristicsLike a note_iR, i ═ 1., | Num _ Cluster |; wherein each infrared reconstructed thermal image contains, in addition to the background area thermal image of the defect-free lesion, thermal reconstruction information characteristic of one type of defect of the complex type.

Step two, downsampling the infrared thermogravimetric image of the defect area, and performing a multi-objective optimization guided filtering optimal weight acquisition layer on the downsampled infrared thermographic image layer, wherein the specific method comprises the following steps of:

step S21, based on the down-sampling infrared thermal image_iR^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_iP^down：

_iH^down＝_i R^down*L

_iS^do^wn＝|_iH^down|*GF

Wherein L is a laplacian filter; non-viable cells_iH^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in the down-sampling thermal image based on the following formula_iP^down：

Wherein the content of the first and second substances,

for downsampling coarse weight maps_iP^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,

is composed of_iP^downThe thermal amplitude value of the kth coordinate point of (1) is fused with the weight value,

is a heat amplitude significance characteristic diagram_iS^downThe radiation significance level value corresponding to the kth coordinate point;

step S22, making a picture based on the downsampled thermal image₁R^down…,_iR^down,…,_{|Num_Cluster|-1}R^downGreat weight map of integration of } and downsampling₁P^down…,_iP^down,…,_{|Num_Cluster|-1}P^downPerforming multi-objective optimization guided filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: sampling thermal images in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. down-sampling infra-red thermal images_iR^downAt the kth coordinate point of

The size of the centered local rectangular window is (2r +1) × (2r +1), and the input-output relationship of the multi-target-oriented filtering is as follows:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downDownsampled output image obtained by performing multi-target guided filtering on input image_iO^downThe nth coordinate point of (a), n is 1, and I 'x J';

is composed of_iR^downThe thermal amplitude value of the down-sampled reconstructed image corresponding to the nth coordinate point, where n is 1..,I′×J′；a_kAnd b_kIs shown in

Centered guided filter window w_kLinear transformation parameters of (I), k ═ 1., I 'x J';

step S222, in order to obtain the fusion optimal weight value of the heat amplitude value corresponding to each coordinate of each reconstructed thermal image, the linear transformation parameter a of the guided filtering is subjected to_kAnd b_kThe method for modeling the multi-objective optimization problem comprises the following specific steps:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_iP^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_iR^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_iP^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_iR^downAnd downsampling fused coarse weight map_iP^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_iR^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the gradient domain fine defect detail texture guide filtering cost function is obtained; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_iR^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is n belongs to I multiplied by J;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kMean value of the thermal amplitude, v, corresponding to the respective coordinate points in_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

the method comprises the steps of determining an optimal linear transformation coefficient for a local LoG operator space noise guide filtering cost function; epsilon is a regularization factor;

is a local LoG edge weight factor, which is defined as follows:

wherein LoG (. cndot.) is a Gaussian edge detection operator, I 'xJ' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_LoG0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_iR^downAnd downsampling the coarse weight map_iP^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a′_k) Preserving a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant dimensional and gradient changes, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing a multi-objective optimization related parameter, where the initialization iteration number g' is 0, and a set of uniformly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,...,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-targets by utilizing a Chebyshev decomposition methodThe problems are as follows:

step S2233, 1.., N for N_P: comparing and updating the speed, local optimum and global optimum solutions according to the particle swarm algorithm, using the vector corresponding to each weight

Direction vector of

Guiding the evolution direction of each population solution, and reserving a non-dominated guided filtering linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment: if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting a guide filter linear transformation coefficient compromise solution with the maximum weight membership degree from the AP, and recording an optimal weight vector group corresponding to the guide filter linear transformation coefficient compromise solution

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

Step three, transferring the multi-target optimal weight proportioning parameters to an original scale infrared thermal image fusion layer for multi-target guiding filtering infrared thermal image fusion, and based on the multi-target optimal weight proportioning parameters

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, decomposing each original infrared reconstruction thermal image except the background area into a base layer infrared thermal image₁B,...,_iB,...,_{|Num_Cluster|}B and a detail layer infrared thermal image₁D,...,_iD,...,_{|Num_Cluster|}D }; reconstruction of thermal images from ith defect region_iR is, for example, i ═ 1., | Num _ Cluster | -1, which is obtained by using the following formula_iBase layer infrared thermal image of R_iB and detail layer infrared thermal image_iD：

_iB＝_iR*Z

_iD＝_iR-_iB

Wherein Z is an average filter;

step S32, obtaining a coarse weight map on the original infrared reconstruction thermal image layer based on the following formula_iP：

_iH＝_i R*L

_iS＝|_iH|*GF

Where L is a laplacian filter and GF is a gaussian low pass filter. Obtaining a thermal amplitude fusion coarse weight map based on the following formula_iP：

Wherein the leaf_iP₁,...,_iP_k,...,_iP_I×JIs a coarse weight map_iThe thermal amplitude values of the respective position coordinates of P fuse the weight values,_iP_k(k is 1, …, I.times.J.) is_iThe thermal amplitude value of the kth coordinate point of P fuses the weight values,_iS_k(k-1, …, I × J) is a heat amplitude significance map_iThe radiation significance level value corresponding to the kth coordinate point in the S;

step S33 based on

Obtaining a raw infrared reconstructed thermal image layerMulti-target oriented filtering optimal filter operator MOGF of surface_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

to ensure the consistency of linear transform coefficients in different guided filtering windowsLinear transformation of coefficient a_kAnd b_kThe following modifications were made:

wherein, | w_nI is the number of coordinate points in the guide filter window with the nth coordinate as the center and is based on the linear transformation coefficient a_kAnd b_kThe expression of the final multi-target guiding filter operator is obtained as follows:

wherein the content of the first and second substances,_iO_nand the thermal amplitude value corresponding to the nth coordinate point in the output image of the multi-target guide filtering is obtained. The operation of filtering by using the obtained multi-target optimal linear transformation coefficient to obtain a multi-target guiding filtering operator is recorded as MOGF_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining the optimal guiding filter operator MOGF by utilizing multi-objective optimization_r,ε(P, R) performing multi-target guiding filtering on the obtained thermal amplitude fusion coarse weight graph on the original thermal image layer to obtain a corrected thermal amplitude fusion weight image of the base layer and the detail layer:

wherein_iW^BAnd_iW^Dfusing an i-th basic layer heat amplitude fusion fine modification weight value graph and an i-th detail layer heat radiation value fusion fine modification weight value graph after fusing the coarse weight graph and performing multi-target guiding filtering,_ip is the ith fusion weight map of thermal radiation values,_ir is the ith reconstructed thermal image, R₁,ε₁,r₂,ε₂Respectively, the parameters of the corresponding steering filter. Finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, map based on the obtained refined detail layer thermal amplitude fusion weight₁W^D,₂W^D,…,_{|Num_Cluster|- 1}W^DMap for integrating weights of heat amplitude of foundation layer₁W^B,₂W^B,…,_{|Num_Cluster|-1}W^BAnd (3) fusing the detail layer thermal image information and the base layer thermal image information among the thermal reconstruction images of different defect areas except the background area to obtain a base layer thermal image and a detail layer thermal image fused with a plurality of pieces of reconstruction thermal image effective information:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, a multi-target oriented filtering fusion image which is fused with a plurality of pieces of reconstructed thermal image defect effective information and simultaneously considers the retention requirement of large-size defects, the retention requirement of detail textures of micro defects and the retention requirement of integral noise elimination in each thermal image is obtained; inputting the high-quality infrared reconstruction fusion image F fused with the characteristics of various complex defects into the infrared thermal image segmentation and defect quantitative analysis steps so as to further extract the quantitative characteristic information of various defects.

In this example, there are two defects on the test piece, namely, the ultra-high-speed center impact pit outer damage defect 1 and the surrounding sputtering type fine damage defect 2 caused by impact shot cracking.

A flow chart of an overall fusion framework for multi-sheet (two for example) infrared thermal image fusion based on two-layer multiobjective optimization and guided filtering is shown in fig. 17.

A flowchart of obtaining the modified weighted image of each image layer by specifically combining the two-layer multi-objective optimization and the guided filtering is shown in fig. 18.

In this example, the result after classifying the transient thermal response set by the BIRCH clustering algorithm is shown in fig. 19.

Based on the BIRCH clustering algorithm, obtaining clustering centers corresponding to various transient thermal response sets as typical characteristic transient thermal response C of various types of damaged areas_Cluster[1]、C_Cluster[2]And C_Cluster[3]. Their respective typical characteristic transient thermal response curves are shown in fig. 20, 21, 22.

After typical characteristic transient thermal response curves of all damage areas of the test piece are obtained, an infrared thermal image reconstruction algorithm is carried out on the basis of the typical characteristic transient thermal response curves, and a reconstructed thermal image of a material perforated area of the material center impact pit is obtained₁R, reconstruction thermal image corresponding to Beijing area of test piece₂Reconstructed thermal image of R and defect 2 temperature point correspondences₃R, as shown in FIG. 23, FIG. 24 and FIG. 25, the respective highlighted defect types are indicated by the symbols in the figures.

The method for solving the optimal guided filtering linear transformation parameters by combining double-layer multi-objective optimization and guided filtering in the invention is used for carrying out multi-objective optimization on the infrared thermal image after down sampling to obtain a series of Pareto optimal non-dominated solutions, a Pareto optimal frontier (PF) is obtained based on the Pareto optimal non-dominated solutions, and an optimal guided filtering thermal image fusion parameter solution is selected based on an optimal weighting membership principle, as shown in FIG. 26.

And after obtaining optimal guided filtering thermal image fusion parameters based on multi-objective optimization and guided filtering, transmitting the weight vector corresponding to the obtained optimal Pareto non-dominated solution to an original scale infrared thermal image fusion layer to obtain a multi-objective guided filtering optimal operator, and performing multi-objective guided filtering operation on the weight images corresponding to the base layer image and the detail layer image which are obtained after infrared reconstruction thermal image decomposition. And obtaining a refined weight map on each image level after multi-target guiding filtering correction. With W₁ ^BRepresenting the refined base layer weight map e,

represents the refined base layer weight graph f, W₁ ^DA refined detail level weight graph g is shown,

the refined base layer weight map h is shown in fig. 27, 28, 29, and 30.

The infrared thermal image fusion operation is performed on each layer of weight image corrected by the double-layer multi-target optimal oriented filtering operator, and the final infrared fusion thermal image of the complex defect is shown in fig. 31. The damage condition characteristics of the defects 1 and 2 can be clearly and simultaneously represented in the graph with high quality, and subsequent image segmentation and defect identification quantitative operation can be better carried out.

In the present embodiment, the extracted features fusing the plurality of types of defects are shown in fig. 31.

It can be seen that the final fused infrared detection image obtained in the embodiment has better detectability for various types of damage.

The number of apparatuses and the scale of the process described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the present invention will be apparent to those skilled in the art.

While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (7)

1. A comprehensive interpretation method for infrared detection characteristics of a large-size test piece is characterized by comprising the following steps:

the method comprises the following steps of firstly, carrying out infrared detection on a large-size test piece to obtain an infrared thermal image sequence of the large-size test piece, and obtaining an infrared thermogravimetric image of the large-size test piece from a plurality of infrared thermal image sequences by utilizing an infrared feature extraction and infrared thermal image reconstruction algorithm;

performing image down-sampling on the infrared thermogravimetric image of the large-size test piece defect area to obtain a down-sampled infrared thermal image containing lower infrared thermal radiation data quantity, and acquiring a thermal amplitude fusion coarse weight map of the down-sampled thermal image based on the down-sampled infrared thermal image; performing multi-objective optimization guided filtering based on the down-sampling thermal image and the down-sampling fusion coarse weight map to obtain a Pareto optimal weight vector; modeling a filter input and filter output relation of the multi-target guiding filter; performing multi-objective optimization problem modeling on linear transformation parameters of the guided filtering to obtain a final leading edge approximate solution set of the linear parameters of the multi-objective guided filtering; optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm; selecting a guide filtering linear transformation coefficient compromise solution with the maximum weighting membership degree from the optimal Pareto optimal solution set based on a weighting membership degree scheme, recording a corresponding optimal weight vector group, thus obtaining the optimal weight ratio of a plurality of comprehensive guide filters, and then transmitting the optimal weight parameters to an original infrared thermal image fusion layer;

thirdly, performing a multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer based on the multi-target optimal weight ratio parameter; decomposing the infrared thermal reconstruction image of the defect area in the large-size impact test piece into a base layer infrared thermal image and a detail layer infrared thermal image; calculating to obtain an initial infrared thermal radiation coarse fusion weight map; acquiring a multi-target oriented filtering optimal filtering operator of an original infrared reconstruction thermal image layer; performing multi-target guiding filtering on the infrared thermal amplitude fusion coarse weight graph of the obtained infrared detection area infrared thermal reconstruction image by using an optimal guiding filtering operator obtained by multi-target optimization to obtain corrected infrared thermal amplitude fusion weight images of the basic layer and the detail layer; finally, normalizing the refined thermal amplitude fusion weight graph; based on the obtained refined detail layer thermal amplitude fusion weight map and the base layer thermal amplitude fusion weight map, detail layer infrared thermal image information and base layer infrared thermal image information among typical type defect infrared thermal reconstruction images of the large-size test piece are fused to obtain a plurality of base layer infrared thermal images and detail layer infrared thermal images fused with effective information of the infrared thermal reconstruction images of the multiple detection areas, and finally the base layer infrared thermal images and the detail layer infrared thermal images after weighted averaging are combined to obtain a final fusion detection infrared thermal image.

2. The method for comprehensively interpreting infrared detection characteristics of a large-size test piece according to claim 1, wherein the step one of acquiring the infrared reconstructed thermal image from the infrared thermal image sequence by using an infrared characteristic extraction and infrared thermal image reconstruction algorithm comprises the following specific steps:

s11, extracting characteristic information of each transient thermal response from a thermal image sequence S acquired by a thermal infrared imager and forming a characteristic matrix Fe; wherein S (I, J, T) represents pixel values of an ith row and a jth column of a T-frame thermal image of the thermal image sequence, T is 1.. T, T is a total frame number, I is 1.. T, I is a total row number, and J is 1.. J, J is a total column number; fe (i, j, f) represents the f (f is 1, …,6) th feature information corresponding to the coordinate position of the i-th row and the j-th column of the feature matrix; the first characteristic information is a thermal amplitude peak value, namely Fe (i, j,1) is max (S (i, j, and): S (i, j, and) represents the temperature change condition of the ith row and the jth column in the whole T frame process; the second characteristic information being the mean value of the thermal amplitudes, i.e.

The 3 rd feature information is coefficient of variation, i.e.

The 4 th characteristic information being the rate of rise, i.e.

Wherein t is_maxRepresenting the frame number corresponding to the heat radiation peak value; the 5 th characteristic information being the rate of descent, i.e.

The 6 th characteristic information is the heat radiation kurtosis and is used for representing the peak or the flatness of the transient thermal response curve, namely

Finally obtaining a characteristic matrix Fe of the thermal image sequence S;

step S12, self-adaptively clustering the feature matrix Fe into | C | classes by using a BIRCH clustering algorithm; constructing a triple clustering characteristic CF, wherein CF is { N, LS, SS }, wherein N is the number of sample points owned by the node, LS is a sum vector of characteristic dimensions of the sample points owned by the node, and SS represents a square sum of the characteristic dimensions of the sample points owned by the node; generating a clustering feature tree based on the CFs, defining the maximum CF number B of the internal nodes, the maximum CF number L of the leaf nodes, and the maximum sample radius threshold T of each CF of the leaf nodes; continuously searching for clustering characteristics meeting the requirement within a hypersphere radius threshold T from a root node, creating a new leaf node under the condition that the number of leaf nodes is less than L, and putting a new sample meeting the condition; judging, checking and splitting leaf nodes to finally obtain a clustering feature tree; screening the clustering feature tree to remove abnormal CF nodes; using a global clustering algorithm to perform clustering repair on all leaf nodes to obtain a clustering feature tree; taking the global clustering center point as a seed, redistributing the data points to the nearest seed, ensuring that repeated data are distributed into the same cluster, and adding a cluster label; synchronizing the clustering Cluster labels to each transient thermal response in the original thermal image sequence to form a Cluster [ h ], wherein h is 1,2,., | Num _ Cluster | represents a category label, and | Num _ Cluster | represents the total number of categories;

step S13, respectively extracting typical characteristic transient thermal responses from different clusters and reconstructing a thermal image based on the typical characteristic transient thermal responses; calculating the clustering center of each category in the clustering result as the typical characteristic transient thermal response of each category of defects:

wherein

Cluster result h for h]The kth of | Num _ Cluster | represents the transient thermal response, Cluster [ h ═ 1]Forming a matrix Y by typical transient thermal responses of various types of defects for the total number of transient thermal responses contained in the h-th clustering result;

the infrared thermal image reconstruction is carried out by utilizing the information of the matrixes Y and S, each frame image of S is extracted into a column vector according to columns and is arranged in time sequence to form an I multiplied by J row and T column two-dimensional image matrix O, and a reconstruction matrix R is obtained based on the following transformation formula:

wherein the content of the first and second substances,

is a matrix of | Num _ Cluster | xT, which is a pseudo-inverse of the matrix Y, O^TThe method is characterized in that the method is a transpose matrix of a two-dimensional image matrix O, and an obtained reconstruction matrix R is | Num _ Cluster | rows and I multiplied by J columns; intercepting each row of the reconstruction matrix R to form an I multiplied by J two-dimensional image to obtain a Num _ Cluster I multiplied by J two-dimensional image which is an infrared reconstruction thermal image containing different thermal response area characteristic information, and taking the non-defect imageBackground area reconstructed thermal image_BAnd R, recording the reconstructed thermal images corresponding to the transient thermal responses of all the class characteristics_iR, i ═ 1., | Num _ Cluster |; wherein each infrared reconstructed thermal image contains, in addition to the background area thermal image of the defect-free lesion, thermal reconstruction information characteristic of one type of defect of the complex type.

3. The method for comprehensively interpreting infrared detection characteristics of a large-size test piece according to claim 1, wherein the steps of performing multiple infrared detections on the large-size test piece to obtain multiple thermal image sequences of the large-size test piece, and obtaining multiple reconstructed infrared thermal images of the large-size test piece from the multiple thermal image sequences by using an infrared characteristic extraction and infrared thermal image reconstruction algorithm comprise:

step S11, using a three-dimensional matrix set { S } for a plurality of thermal image sequences acquired from a thermal infrared imager¹,...,Sⁱ,...,S^|C|Denotes wherein SⁱRepresenting a thermal image sequence obtained by an infrared thermal imager in the ith infrared detection, and | C | representing the total number of the thermal image sequences; sⁱ(M, N, T) represents a temperature value at the coordinate position of the mth row and the nth column of the tth frame thermal image in the ith thermal image sequence, wherein T is 1, the.

Step S12, for the ith thermal image sequence SⁱExtracting the ith thermal image sequence S by utilizing a transient thermal response data extraction algorithm based on block variable step lengthⁱTransient thermal response data set X of mesovalueⁱ(g) (ii) a Passing the ith thermal image sequence S through a thresholdⁱDecomposition into K different data blocks_kSⁱ(m ', n', t) wherein k represents the ith thermal image sequence SⁱM ', n', t respectively represent temperature values at the coordinate positions of the m 'th row, the n' th column and the t-th frame of the kth sub-data block, and then define the ith thermal image sequence S according to the temperature change characteristics in different data blocksⁱStep size of search line in k-th data block_kRSSⁱAnd column step size_kCSSⁱ(ii) a Based on different search steps, K1, K, in different data blocks, comparing correlation coefficients between data points, and searching for a series of correlation coefficients greater than a threshold THC_crAnd adding the ith thermal image sequence SⁱTransient thermal response data set X in (1)ⁱ(g)；

Step S13, using DPC clustering algorithm based on density peak to classify the ith thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) Adaptive clustering of transient thermal responses in (1); firstly, randomly calculating the distance between two transient thermal response samples; calculating the local density rho of each transient thermal response sample according to the truncation distance_i(ii) a Calculating the distance delta of each transient thermal response sample to the transient thermal response sample point which has larger local density and is closest to the transient thermal response sample point_i(ii) a Using p_iAnd delta_iDrawing a decision graph and dividing rho_iAnd delta_iThe points that are all relatively high are marked as cluster centers, p_iRelatively low but delta_iRelatively high points are marked as noise; distributing the rest transient thermal response sample points to the nearest neighbor cluster of the sample points with the density larger than that of the sample points to obtain the final transient thermal response cluster division, and dividing the thermal image sequence SⁱSet of transient thermal responses Xⁱ(g) Adaptive clustering to form a set of clusters

Wherein H represents a defect type label, and H represents the total number of types of complex defects existing in the current infrared detection area;

step S14, respectively extracting representative characteristic transient thermal responses of various complex defects in the ith detection area from different clusters and reconstructing thermal images based on the transient thermal responses; calculating the clustering center of each category in the clustering result as the representative characteristic transient thermal response of each category of defects:

wherein

For the h-th clustering result_X(g)Cluster[h]H-1, …, the kth transient thermal response in H_X(g)Cluster[h]L is the total number of transient thermal responses contained in the h-th clustering result, and a matrix Y is formed by the representative transient thermal responses of all the types of defectsⁱ；

Using matrix YⁱAnd SⁱThe information is subjected to infrared thermal image reconstruction, and the ith thermal image sequence S is obtainedⁱEach frame image is extracted into a column vector according to columns and arranged in time sequence to form a two-dimensional image matrix O of M multiplied by N rows and T columnsⁱObtaining a heat amplitude value reconstruction matrix R of the ith detection based on the following transformation formulaⁱ：

Wherein the content of the first and second substances,

is H × T matrix, and is a representative transient thermal response matrix YⁱPseudo-inverse matrix of (O)ⁱ)^TIs a two-dimensional image matrix OⁱTranspose matrix, obtaining reconstruction matrix of H rows and M multiplied by N columns, intercepting reconstruction matrix RⁱForming an M multiplied by N two-dimensional image for each line to obtain H M multiplied by N two-dimensional images, namely reconstructing thermal images containing different thermal response area characteristic information in the thermal image sequence obtained by the ith infrared detection, and recording the non-defect background area reconstruction thermal images as_BR, recording the reconstructed thermal image corresponding to each type of defect area as_hR, H1, wherein each reconstructed thermal image contains, in addition to the thermal image of the background area free of defect lesions, the characteristic thermal reconstruction information of one type of defect among the complex types of defects currently detected, and the reconstructed thermal image of the type of defect in the detected area obtained in the ith infrared detection is recorded as the reconstructed thermal image of the type of defect in the detected area_Def.(i)R；

Step S15, ifi +1 and repeating the steps S12-S14 until all types of defect reconstruction thermal images in the current detected area are obtained from a plurality of thermal image sequences obtained by multiple detections respectively, then calculating PSNR (peak signal-to-noise ratio) values of all types of defect reconstruction thermal images in the current area, and selecting typical type defect reconstruction thermal images in all detection areas based on the maximum principle of PSNR (peak signal-to-noise ratio), namely obtaining a typical type defect reconstruction thermal image set in each detection area of the large-size test piece_Def.(1)R,…,_Def.(i)R,…,_Def.(|C|)R }, wherein_Def.(i)R represents a typical type of defect reconstruction thermal image of the detected region in the ith thermal image sequence, i 1.

4. The comprehensive interpretation method for infrared detection characteristics of large-size test piece according to claim 2, wherein the step of two pairs of (| C | -1) infrared reconstructed images excluding the thermal image of the background-free area₁R,…,_iR,…,_|C|-1R, down sampling each image to obtain a down sampled thermal image containing a lower amount of infrared thermal radiation data₁R^down…,_iR^down,…,_{|C|- 1}R^downAnd the size of the down-sampled thermal image is I 'xJ', and the following multi-target oriented filtering weight acquisition layer steps are executed based on the down-sampled thermal image, wherein the specific method comprises the following steps:

step S21, based on the down-sampling infrared thermal image_iR^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_iP^down：

_iH^down＝_iR^down*L

_iS^down＝|_iH^down|*GF

Wherein L is a laplacian filter; non-viable cells_iH^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in the down-sampling thermal image based on the following formula_iP^down：

Wherein the content of the first and second substances,

for downsampling coarse weight maps_iP^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,_iP_k ^downis composed of_iP^downThe thermal amplitude value of the kth coordinate point of (a) is fused with a weight value, k 1, I 'x J',

is a heat amplitude significance characteristic diagram_iS^downA radiation significance level value corresponding to the kth coordinate point, k being 1., I 'x J';

step S22, making a picture based on the downsampled thermal image₁R^down…,_iR^down,…,_{|Num_Cluster|-1}R^downGreat weight map of integration of } and downsampling₁P^down…,_iP^down,…,_{|Num_Cluster|-1}P^downPerforming multi-objective optimization guided filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: sampling thermal images in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. down-sampling infra-red thermal images_iR^downAt the kth coordinate point of

A centered local rectangular window, k is 1., I 'x J', which has a size of (2r +1) × (2r +1), the input-output relationship of the multi-target guided filtering is:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_iR^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_iP^downDownsampled output image obtained by performing multi-target guided filtering on input image_iO^downThe nth coordinate point of (a), n is 1, and I 'x J';

is composed of_iR^downThe downsampled reconstructed image thermal amplitude value corresponding to the nth coordinate point of (a), n is 1. a is_kAnd b_kIs shown in

Centered guided filter window w_kLinear transformation parameters of (I), k ═ 1., I 'x J';

step S222, in order to obtain the fusion optimal weight value of the heat amplitude value corresponding to each coordinate of each reconstructed thermal image, the linear transformation parameter a of the guided filtering is subjected to_kAnd b_kThe method for modeling the multi-objective optimization problem comprises the following specific steps:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_iP^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_iR^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_iP^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_iR^downAnd downsampling fused coarse weight map_iP^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_iR^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the gradient domain fine defect detail texture guide filtering cost function is obtained; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_iR^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_iR^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is n belongs to I multiplied by J;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_iR^downAnd downsampling thermal amplitude fused coarse weight map_iP^downIs integrated in a rectangular window w_kHeat corresponding to each coordinate point inMean value of the amplitude v_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_iP^downAnd infrared down-sampling thermal images_iR^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

the method comprises the steps of determining an optimal linear transformation coefficient for a local LoG operator space noise guide filtering cost function; epsilon is a regularization factor;

is a local LoG edge weight factor, which is defined as follows:

wherein LoG (. cndot.) is a Gaussian edge detection operator, I 'xJ' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_LoG0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_iR^downAnd downsampling the coarse weight map_iP^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a′_k) Preserving a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant dimensional and gradient changes, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing a multi-objective optimization related parameter, where the initialization iteration number g' is 0, and a set of uniformly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,...,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-target problems by utilizing a Chebyshev decomposition method:

step S2233, 1.., N for N_P: comparing and updating the speed, local optimum and global optimum solutions according to the particle swarm algorithm, using the vector corresponding to each weight

Direction vector of

Guiding the evolution direction of each population solution, and reserving a non-dominated guided filtering linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment: if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting a guide filter linear transformation coefficient compromise solution with the maximum weight membership degree from the AP, and recording an optimal weight vector group corresponding to the guide filter linear transformation coefficient compromise solution

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

5. The method for comprehensively interpreting infrared detection characteristics of large-size test pieces according to claim 3, wherein the step of processing a total of | C | typical type defect infrared reconstructed images of each detection area of two pairs of large-size impact test pieces_Def.(1)R,…,_Def.(i)R,…,_Def.(|C|)R, down sampling each image to obtain a down sampled thermal image containing a lower amount of infrared thermal radiation data_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(C)R^downAnd (4) the size dimension of the down-sampled thermal image is M 'multiplied by N', and the following multi-target guiding filtering weight acquisition layer steps are executed based on the down-sampled thermal image:

step S21, based on the down-sampling infrared thermal image_Def.(i)R^downObtaining a thermal amplitude fusion coarse weight map in a down-sampled thermal image_Def.(i)P^down：

_Def.(i)H^down＝_Def.(i)R^down*L

_Def.(i)S^down＝|_Def.(i)H^down|*GF

Wherein L is a laplacian filter; non-viable cells_Def.(i)H^downL is the absolute value of the high-pass thermal image, GF is a gaussian low-pass filter; obtaining a heat amplitude fusion coarse weight graph in a typical type defect downsampling thermal image of the ith detection area based on the following formula_Def.(i)P^down：

Wherein the content of the first and second substances,

for downsampling coarse weight maps_Def.(i)P^downThe thermal amplitude values of the respective position coordinates of (a) and (b) are fused with the weight values,

is composed of_Def.(i)P^downThe thermal amplitude value of the kth coordinate point of (1) is fused with the weight value,

is a heat amplitude significance characteristic diagram_Def.(i)S^downA radiation significance level value corresponding to the kth coordinate point, k being 1., M 'x N';

step S22, making a picture based on the downsampled thermal image_Def.(1)R^down,…,_Def.(i)R^down,…,_Def.(|C|)R^downGreat weight map of integration of } and downsampling_Def.(1)P^down,…,_Def.(i)P^down,…,_Def.(|C|)P^downPerforming multi-objective optimization guide filtering to obtain Pareto optimal weight vectors, wherein the specific method comprises the following steps:

step S221, modeling of filter input and filter output relation of multi-target guiding filtering: infrared sampling thermal image of typical type defect in ith detection area_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downPerforming multi-target guided filtering for an input image; in the process of multi-target guide filtering, a guide filtering window w is defined_kFor guiding the image, i.e. downsampling the infrared thermal image Def^downWith the kth coordinate point Def. (i) R in (e)_k ^downA centered local rectangular window, k is 1., M 'x N', which has a size of (2r +1) × (2r +1), the input/output relationship of the multi-target guided filtering is:

wherein the content of the first and second substances,

representing thermal images sampled in infrared_Def.(i)R^downTo guide the image, the undersampled thermal amplitude fuses the coarse weight map_Def.(i)P^downTypical type defect downsampling output image of i-th detection area obtained by carrying out multi-target guide filtering on input image_Def.(i)O^downThe nth coordinate point of (a) corresponds to a pilot filter output value, N is 1.

N is 1, M 'x N' is_Def.(i)R^downThe thermal amplitude value of the down-sampling reconstructed image corresponding to the nth coordinate point; a is_kAnd b_kIs shown in

k＝1, M 'x N' centered guided filtering window w_kLinear transformation parameters within;

step S222, in order to obtain the fusion optimal weight value of the thermal amplitude value corresponding to each coordinate of each reconstructed thermal image of each typical defect type of each infrared detection area, the linear transformation parameter a of the guide filtering is subjected to_kAnd b_kModeling a multi-objective optimization problem:

step S2221, based on down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining the edge characteristic perception weighted guide filtering cost function of the infrared large-size defect at each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the large-size defect perception filtering cost function is obtained;

is a weight map_Def.(i)P^downThe thermal radiation fusion weight value corresponding to the nth coordinate point; epsilon is a regularization factor;

is an edge perceptual weighting factor, which is defined as follows:

wherein the content of the first and second substances,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The variance, ζ, of the heat radiation values corresponding to the respective coordinate points in a 3 × 3 window centered on the coordinate point is a very small constant having a magnitude of (0.001 × DR: (b:)_Def.(i)P^down))²DR (-) is the dynamic range of the image, and the following expression of the optimal linear transformation coefficient is obtained by minimizing the cost function:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kThe average value of the thermal amplitude values corresponding to each coordinate point in the inner,

is the hadamard product of the matrix,

and

separately representing down-sampled infrared thermal images_Def.(i)R^downAnd downsampling fused coarse weight map_Def.(i)P^downIn a rectangular window w_kThe mean value of the interior of the cell,

representing sampled infrared thermal images_Def.(i)R^downIn a rectangular window w_kThe variance of the thermal amplitude corresponding to each coordinate point in the interior;

step S2222, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining gradient domain infrared fine size defect detail texture guide filtering cost function on each coordinate point position

Wherein the content of the first and second substances,

and

the optimal linear transformation coefficient determined by the gradient domain fine defect detail texture guide filtering cost function is obtained; epsilon is a regularization factor; v is_kTo adjust a_kA factor of (d);

is a gradient domain multi-window edge perception weight, which is defined as follows:

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering window w with coordinate point as center_kThermal amplitude standard deviation, v, corresponding to each coordinate point in_kIs defined as follows:

wherein eta is

Representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

The standard deviation of the thermal amplitude corresponding to each coordinate point in a 3 x 3 window centered on the coordinate point,

representing down-sampled infrared thermal images_Def.(i)R^downIn the middle, in

Guide filtering rectangular window w with coordinate point as center_nThe thermal amplitude standard deviation corresponding to each coordinate point in the thermal insulation material is N belongs to M 'multiplied by N';

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein the content of the first and second substances,

representation of downsampled infrared thermal images_Def.(i)R^downAnd downsampling thermal amplitude fused coarse weight map_Def.(i)P^downIs integrated in a rectangular window w_kMean value of the thermal amplitude, v, corresponding to the respective coordinate points in_kTo adjust a_kA factor of (d);

step S2223, based on the down-sampling heat amplitude value fusion coarse weight chart_Def.(i)P^downAnd infrared down-sampling thermal images_Def.(i)R^downDefining local LoG operator space noise elimination guide filtering cost function

Wherein the content of the first and second substances,

and

the method comprises the steps of determining an optimal linear transformation coefficient for a local LoG operator space noise guide filtering cost function; epsilon is a regularization factor;

is a local LoG edge weight factor, which is defined as follows:

wherein LoG (. cndot.) is a Gaussian edge detection operator, M 'xN' is the total number of coordinate points of the infrared down-sampling thermal image, |. cndot ] is an absolute value operation, and delta_LoG0.1 times the maximum value of the LoG image;

by minimizing gradient domain oriented filtering cost function

To obtain

And

the calculation formula of (2) is as follows:

wherein

And

respectively representing infrared down-sampled thermal images_Def.(i)R^downAnd downsampling the coarse weight map_Def.(i)P^downIn a rectangular window w_kThe average value of the thermal amplitude corresponding to each coordinate point in the inner space;

step S2224, 3 cost functions are optimized simultaneously, and the following multi-objective optimization problem is established:

Minimize F(a_k')＝[_Inf.SigE₁(a_k'),_Inf.MinE₂(a_k'),_Inf.NoiE₃(a_k')]^T

wherein, a_k' is the k-th directed filter window w_kThe linear transformation coefficients of (1) are,_Inf.SigE₁(a_k') remains the fusion cost function for large-size defect edges in infrared thermal images with significant gradient changes,_Inf.MinE₂(a_k') remaining a fusion cost function for the fine defect detail texture of infrared thermal images with insignificant size and gradient variation, E₃(a_k') is a cost function for sensing and eliminating the noise information of the infrared thermal image;

step S223, optimizing the multi-objective optimization problem by using a multi-objective optimization method based on a Chebyshev decomposition method and particle swarm, wherein the specific method comprises the following steps:

step S2231, initializing multi-objective optimization related parameters; the number of initialization iterations g' is 0, and a set of evenly distributed weight vectors

Wherein L is 3, the total number of the guided filtering cost functions considered simultaneously,

pareto optimal reference point for initializing guide filtering linear transformation coefficient_ir＝{_ir₁,...,_ir₃}，

Is the l-th oriented filtering cost function E_l(a_k') a corresponding reference point;_iAP (0) ═ Φ; maximum number of iterations g'_max；

Initializing the particle swarm related parameters of the nth guided filtering linear transformation coefficient population;

step S2232, utilizing

Decomposing an original multi-target problem into a series of scalar sub-target problems by utilizing a Chebyshev decomposition method

Step S2233, 1.., N for N_PComparing and updating speed, local optimal solution and global optimal solution according to a particle swarm algorithm, and reserving a non-dominated guided filter linear transformation coefficient solution set; n is N +1, N is less than or equal to N_PThen g '═ g' + 1;

step S2234, evolution termination judgment, if g 'is less than or equal to g'_maxThen step S2233 is repeated if g '> g'_maxThen the final leading edge approximate solution set of the linear parameters of the multi-target guiding filtering is obtained_iAP；

Step S224, based on the weighting membership degree scheme, from the optimal Pareto optimal solution set_iSelecting a guide filter linear transformation coefficient compromise solution with the maximum weight membership degree from the AP, and recording an optimal weight vector group corresponding to the guide filter linear transformation coefficient compromise solution

Thus, the optimal weight ratio of the integrated multiple guide filters is obtained, and then the optimal weight parameters are transmitted to the original infrared thermal image fusion layer.

6. The comprehensive interpretation method for infrared detection characteristics of large-size test piece according to claim 4Method characterized in that said multi-objective based optimal weight proportioning parameters

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, decomposing each original infrared reconstruction thermal image except the background area into a base layer infrared thermal image₁B,...,_iB,...,_{|Num_Cluster|}B and a detail layer infrared thermal image₁D,...,_iD,...,_{|Num_Cluster|}D }; reconstruction of thermal images from ith defect region_iR is, for example, i ═ 1., | Num _ Cluster | -1, which is obtained by using the following formula_iBase layer infrared thermal image of R_iB and detail layer infrared thermal image_iD：

_iB＝_iR*Z

_iD＝_iR-_iB

Wherein Z is an average filter;

step S32, obtaining a coarse weight map on the original infrared reconstruction thermal image layer based on the following formula_iP：

_iH＝_iR*L

_iS＝|_iH|*GF

Wherein L is Laplace filter, GF is a Gaussian low-pass filter, and the thermal amplitude fusion coarse weight map is obtained based on the following formula_iP：

Wherein, { iP₁,...,_iP_k,...,_iP_I×JIs a coarse weight map_iThe thermal amplitude values of the respective position coordinates of P fuse the weight values,_iP_kis composed of_iThe thermal amplitude value of the kth coordinate point of P fuses the weight values,_iP_kiS_k(k-1, …, I × J) is a heat amplitude significance map_iA radiation significance level value corresponding to a kth coordinate point in the S, wherein k is 1.

Step S33 based on

Multi-target guiding filtering optimal filter operator MOGF for obtaining original infrared reconstruction thermal image layer_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

in order to ensure the consistency of the linear transformation coefficient in different guide filtering windows, the linear transformation coefficient a is used_kAnd b_kThe following modifications were made:

wherein, | w_nI is the number of coordinate points in the guide filter window with the nth coordinate as the center and is based on the linear transformation coefficient a_kAnd b_kThe expression of the final multi-target guiding filter operator is obtained as follows:

wherein the content of the first and second substances,_iO_nfor the thermal amplitude corresponding to the nth coordinate point in the output image of the multi-target oriented filtering, the operation of filtering by using the obtained multi-target optimal linear transformation coefficient to obtain a multi-target oriented filtering operator is recorded as MOGF_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining the optimal guiding filter operator MOGF by utilizing multi-objective optimization_r,ε(P, R) performing multi-target guided filtering on the obtained thermal amplitude fusion coarse weight map on the original thermal image level to obtain a modified base layer and detailsThermal amplitude fusion weight image of layer:

wherein_iW^BAnd_iW^Dfusing an i-th basic layer heat amplitude fusion fine modification weight value graph and an i-th detail layer heat radiation value fusion fine modification weight value graph after fusing the coarse weight graph and performing multi-target guiding filtering,_ip is the ith fusion weight map of thermal radiation values,_ir is the ith reconstructed thermal image, R₁,ε₁,r₂,ε₂Respectively, the parameters of the corresponding guide filter; finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, map based on the obtained refined detail layer thermal amplitude fusion weight₁W^D,₂W^D,…,_{|Num_Cluster|-1}W^DMap for integrating weights of heat amplitude of foundation layer₁W^B,₂W^B,…,_{|Num_Cluster|-1}W^BAnd (3) fusing the detail layer thermal image information and the base layer thermal image information among the thermal reconstruction images of different defect areas except the background area to obtain a base layer thermal image and a detail layer thermal image fused with a plurality of pieces of reconstruction thermal image effective information:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, a multi-target oriented filtering fusion image which is fused with a plurality of pieces of reconstructed thermal image defect effective information and simultaneously considers the retention requirement of large-size defects, the retention requirement of detail textures of micro defects and the retention requirement of integral noise elimination in each thermal image is obtained; inputting the high-quality infrared reconstruction fusion image F fused with the characteristics of various complex defects into the infrared thermal image segmentation and defect quantitative analysis steps so as to further extract the quantitative characteristic information of various defects.

7. The comprehensive interpretation method for infrared detection characteristics of large-size test pieces according to claim 5, wherein the third step is based on multi-target optimal weight proportioning parameters

The method for performing the multi-target guiding filtering fusion algorithm on the original infrared reconstruction thermal image layer comprises the following steps:

step S31, a total | C | typical type defect infrared reconstruction image of each detection area in large-size impact test piece_Def.(1)R,...,_Def.(i)R,...,_Def.(|C|)Each of R is decomposed into a base layer infrared thermal image { inf.base [ Def. (1)],...,Inf.Base[Def.(i)],...,Inf.Base[Def.(|C|)]And a detailed layer infrared thermal image { inf],...,Inf.Detail[Def.(i)],...,Inf.Detail[Def.(|C|)]}; taking the reconstruction thermal image Def. (i) R of the typical type defect of the ith detection area as an example, the infrared thermal image of the typical type defect base layer and the infrared thermal image Inf.base [ Def. (i) of the Def. (1) R are obtained by using the following formula]And inf]：

Inf.Base[Def.(i)]＝_Def.(i)R*Z

Inf.Detail[Def.(i)]＝_Def.(i)R-Inf.Base[Def.(i)]

Wherein Z is an average filter, i ═ 1., | C |;

step S32, obtaining an initial heat radiation coarse fusion weight map based on the following formula:

_Def.(i)H＝_Def.(i)R*L

_Def.(i)S＝|_Def.(i)H|*GF

wherein L is Laplace filter, GF is a Gaussian low-pass filter, and the thermal amplitude fusion coarse weight map is obtained based on the following formula_Def.(i)P：

Wherein the leaf_Def.(i)P₁,…,_Def.(i)P_k,…,_Def.(i)P_M×NIs a coarse weight map_Def.(i)The thermal amplitude values of the respective position coordinates of P fuse the weight values,_Def.(i)P_kis composed of_Def.(i)The thermal amplitude value of the kth coordinate point of P fuses the weight values,_Def.(i)S_kis a heat amplitude significance characteristic diagram_Def.(i)A radiation significance level value corresponding to a kth coordinate point in the S, wherein k is 1.

Step S33 based on

Multi-target guiding filtering optimal filter operator MOGF for obtaining original infrared reconstruction thermal image layer_r,ε(P, R), wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

optimal weight parameters obtained by the input weight acquisition layer

Transmitting the obtained optimal weight vector to an original infrared reconstruction thermal image multi-target guiding filtering layer to obtain a final cost function E of the multi-target guiding filtering₄Comprises the following steps:

substituting into a specific function form to obtain the final linear transformation coefficient a_kThe final expression of (c) is:

wherein the content of the first and second substances,

representing the reconstructed image R in a rectangular guided filter window w_kInner pixel value variance, μ_k,PRepresenting the heat amplitude fused coarse weight image P in a rectangular window w_kMean of inner pixels, mu_k,RRepresenting the reconstructed thermal image R in a rectangular window w_kThe mean value of the pixels in the interior,

representing the Hadamard product of the constructed thermal image R and the coarse-weighted image P in a rectangular window w_kMean value of inner pixel points;

the linear transformation coefficient b_kThe final expression of (c) is:

b_k＝μ_k,P-a_kμ_k,I

in order to ensure the consistency of the linear transformation coefficient in different guide filtering windows, the linear transformation coefficient a is used_kAnd b_kThe following modifications were made:

wherein, | w_nAnd l is the number of coordinate points in the guide filtering window with the nth coordinate as the center, and the expression of the final multi-target guide filtering operator is as follows:

wherein the content of the first and second substances,_Def.(i)R_nfusing and refining weight values for the thermal amplitude values corresponding to the nth coordinate point in the output image of the multi-target guiding filtering; the operation of filtering the weight graph of the infrared reconstruction thermal image of the ith infrared detection area by using the obtained multi-target optimal linear transformation coefficient through a multi-target guiding filtering operator is recorded as

Wherein R is the size of a guide filtering window, epsilon is a regularization parameter, P is a thermal amplitude fusion coarse weight image, and R is an infrared reconstruction image;

step S34, obtaining optimal guiding filter operator by utilizing multi-objective optimization

And performing multi-target guiding filtering on the thermal amplitude fusion coarse weight graph of the obtained infrared reconstruction thermal image of the ith infrared detection area to obtain a corrected thermal amplitude fusion weight image of the base layer and the detail layer:

wherein WM.Base [ Def. (i)]And wm]Fusion essence of thermal amplitude values of base layers of infrared reconstruction thermal images of typical type defects of ith infrared detection area after multi-target guiding filtering is carried out on fusion coarse weight graphFusing the fine weight value map with the detail layer thermal radiation value of the infrared reconstruction thermal image of the ith infrared detection area,_Def.(i)p is a heat radiation value fusion coarse weight map of the infrared reconstruction thermal image of the ith infrared detection area,_Def.(i)r is the infrared reconstructed thermal image of the ith infrared detection area, R₁,ε₁,r₂,ε₂Respectively corresponding parameters of the guide filter, and finally, normalizing the refined thermal amplitude fusion weight graph;

step S35, based on the obtained fine-modified infrared thermal amplitude fusion weight map of the detail layer of the typical type defect in each infrared detection area { wm.detail [ Def. (1) ], wm.detail [ Def. (i) ], wm.detail [ Def. (| C |) ] and the infrared thermal amplitude fusion weight map of the base layer { wm.base [ Def. (1) ], wm.base [ Def., (i) ], wm.base [ Def., (| wm.base |) ], and the thermal image information of the base layer and the thermal image information of the detail layer between the thermal reconstruction images of the typical type defects in different detection times in the large-size test piece are fused, so as to obtain the thermal image of the base layer and the thermal image of the detail layer fused with the effective information of the multiple detection areas:

and finally, combining the base layer thermal image and the detail layer thermal image after weighted averaging to obtain a final fusion detection infrared thermal image:

therefore, the infrared detection fusion thermal image fusing the effective information of the reconstruction thermal image defects of the typical type defects of a plurality of infrared detection areas of the large-size test piece is obtained, the infrared fusion thermal image integrates the excellent characteristics of a plurality of guide filters by utilizing a multi-objective optimization algorithm, multiple times of infrared detection are carried out, the typical type defects of different areas are fused together, the high-quality simultaneous imaging of the defects of the large-size pressure container is realized, and the high-quality infrared reconstruction fusion image F simultaneously fusing the typical characteristics of the defects of the plurality of detection areas is input into the infrared thermal image segmentation and defect quantitative analysis steps, so that the quantitative characteristic information of various defects is further extracted.

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