CN109767437B - Infrared thermal image defect feature extraction method based on k-means dynamic multi-target - Google Patents

Infrared thermal image defect feature extraction method based on k-means dynamic multi-target Download PDF

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CN109767437B
CN109767437B CN201910019826.5A CN201910019826A CN109767437B CN 109767437 B CN109767437 B CN 109767437B CN 201910019826 A CN201910019826 A CN 201910019826A CN 109767437 B CN109767437 B CN 109767437B
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transient thermal
thermal response
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CN109767437A (en
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程玉华
殷春
薛婷
黄雪刚
张昊楠
陈凯
石安华
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a k-means dynamic multi-target-based infrared thermal image defect feature extraction method, which comprises the steps of selecting transient thermal response of pixel points by changing the step length of a thermal image sequence, classifying by adopting FCM (fuzzy C-means) to obtain the category of the transient thermal response of each pixel point, constructing a corresponding multi-target function by considering the similarity of the pixel value of each category of pixel points and the similar pixel points and the difference of different categories of pixel points, providing a guide direction for population evolution by a prediction mechanism after each environmental change, helping a multi-target optimization algorithm to quickly respond to new changes to obtain a dimension reduction result of the thermal image sequence, and finally extracting the defect features of the infrared thermal image by using a pulse coupling neural network, thereby realizing the accurate selection of representing the transient thermal response (temperature points) and ensuring the accuracy of defect feature extraction, meanwhile, the calculation consumption of obtaining each category of information representing transient heat under a dynamic environment is reduced.

Description

Infrared thermal image defect feature extraction method based on k-means dynamic multi-target
Technical Field
The invention belongs to the technical field of defect detection, and particularly relates to an infrared thermal image defect feature extraction method based on k-means dynamic multi-target.
Background
The infrared thermal image detection technology obtains the structural information of the surface of the material and the surface below the surface of the material by controlling a thermal excitation method and measuring the temperature field change of the surface of the material, thereby achieving the purpose of detection. 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 the temperature field information is converted into a thermal image sequence to be displayed. Because the thermal image sequence obtained by the thermal infrared imager has huge data volume and strong noise interference, the thermal image sequence needs to be subjected to feature extraction in order to obtain a better detection effect.
When processing a thermal image sequence, there are methods based on single-frame image processing and also methods based on image sequence processing. The method based on single-frame image processing only considers the temperature distribution information of the test piece at a certain moment, and cannot reflect the temperature conditions of the test piece at different moments, so that the obtained processing result is incomplete and one-sided. Methods based on image sequence processing have therefore received extensive attention and research.
Infrared thermography inspection is most often performed using eddy current thermography. According to the law of electromagnetic induction, when an induction coil which is introduced with high-frequency alternating current is close to a conductor test piece (referred to as a test piece for short), an eddy current is induced on the surface of the test piece. If a defect is found in the test piece, the eddy current is forced to bypass the defect and change the flow direction of the defect, so that the density of the eddy current in the tested piece is changed. According to the Joule law, eddy current is converted into Joule heat in a test piece, so that heat generated in the test piece is uneven, a high-temperature area and a low-temperature area are generated, due to temperature difference, the heat in the high-temperature area is transferred to the low-temperature area through heat conduction, the temperature of different areas of the test piece is changed, the change process of the temperature of the test piece is collected through an infrared thermal imager, then the collected thermal image sequence is sent to a computer for analysis and processing, the relevant information of the test piece is obtained, and qualitative and quantitative detection of defects is achieved.
In the Chinese invention patent application published in 2018, 10, month and 30, and having publication number CN108712069A and named as a high-pressure container thermal imaging defect detection method based on line variable step length segmentation, the clustering result is subjected to dimensionality reduction, and a two-dimensional matrix obtained by dimensionality reduction is linearly transformed with an original image sequence to extract defect characteristics. In the process, the representative temperature points of each class are obtained by utilizing the correlation degree between different classes, but the similarity between the representative temperature points (transient thermal response) and the temperature points of the same class is not researched, and the selected representative temperature points are not enough for characterizing the class, so that the targets of the difference and the similarity are considered at the same time. In addition, the method searches for the thermal response temperature points with regional representatives in each category, the temperature points are the thermal response data which are screened from the centers of other clusters and have the largest distance from the centers of the other clusters in the corresponding category, the thermal response data of the representative temperature points of all the categories form a two-dimensional matrix, and then the representative temperature points represent incomplete information representation of the corresponding category, so that the defect characteristics extracted through linear transformation are inaccurate, and therefore certain accuracy cannot be achieved.
In the real world, many multi-objective optimization problems are influenced by the environment, and the optimization problems themselves, independent variables and the like can change along with the change of the environment. In the process, by using a multi-objective optimization method, the difference among different categories and the similarity of the categories are comprehensively considered, approximate leading edge solutions of the temperature points of each category are obtained, and one temperature point is randomly selected from the leading edge solutions to serve as a representative temperature point. Under ideal conditions without consideration of factors of the environment, representative temperature points capable of comprehensively characterizing each category of information are obtained, but if in a dynamic environment, the entire calculation step is performed in each environment, time consumption is large, and reaction is slow.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for extracting the defect characteristics of an infrared thermal image based on k-means dynamic multi-target, which improves the accuracy of extracting the defect characteristics and reduces the calculation consumption of acquiring various types of information to represent transient thermal response (temperature points) in a dynamic environment.
In order to achieve the aim, the invention discloses an infrared thermal image defect feature extraction method based on k-means dynamic multi-target, which is characterized by comprising the following steps of:
(1) representing a thermal image sequence acquired by the thermal infrared imager by using a three-dimensional matrix S, wherein elements S (i, j, t) represent pixel values of an ith row and a jth column of a t-frame thermal image of the thermal image sequence;
(2) selecting the maximum pixel value S (i) from the three-dimensional matrix Szz,jzz,tzz) Wherein i iszz、jzzAnd tzzRespectively representing the row number of the row where the pixel point with the maximum pixel value is located, the column number of the column where the pixel point is located and the frame number of the frame where the pixel point is located;
(3) t for three-dimensional matrix SzzFrame, select jthzzSelecting P pixel value jumping points according to the change of pixel values (namely temperature values), wherein the jumping points are positioned between two jumping pixel value pixel points, and dividing the three-dimensional matrix S according to rows by the jumping points to obtain P +1 row data blocks;
at the p-th line data block SpWhere (P ═ 1, 2., P +1), the maximum pixel value is found, denoted as
Figure BDA0001940385260000021
Wherein the content of the first and second substances,
Figure BDA0001940385260000022
respectively representing the p-th line data block SpThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure BDA0001940385260000023
Corresponding transient thermal response is
Figure BDA0001940385260000031
T is the total number of S frames of the three-dimensional matrix;
setting a p-th line data block SpHas a temperature threshold of THREpCalculating transient thermal response
Figure BDA0001940385260000032
From the maximum pixel value, i.e. the maximum value of the temperature
Figure BDA0001940385260000033
Transient thermal response corresponding to pixel values of pixels in near and far rows where pixels are located
Figure BDA0001940385260000034
Correlation between RebB, sequentially taking 1,2 and judging the correlation RebWhether or not less than temperature threshold THREpWhen the distance b is less than the p-th row data block line data block S, the calculation is stopped, and at the moment, the pixel point distance b ispLine step length of (1), noted as CLp；
(4) T for three-dimensional matrix SzzFrame, select the ithzzIn the row, Q pixel value jump points are selected according to the change of pixel values (namely temperature values), the jump points are positioned between two jump pixel value pixel points, and the jump points divide the three-dimensional matrix S according to columns to obtain Q +1 column data blocks;
in the q column data block SqWhere (Q ═ 1, 2., Q +1), the maximum pixel value is found, denoted as
Figure BDA0001940385260000035
Wherein the content of the first and second substances,
Figure BDA0001940385260000036
respectively representing the q-th column data block SqThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure BDA0001940385260000037
Corresponding transient thermal response is
Figure BDA0001940385260000038
T is the total number of S frames of the three-dimensional matrix;
setting a qth column data block SqHas a temperature threshold of THREqCalculating transient thermal response
Figure BDA0001940385260000039
From the maximum pixel value, i.e. the maximum value of the temperature
Figure BDA00019403852600000310
Transient thermal response corresponding to pixel values of pixels from near to far in row of pixel
Figure BDA00019403852600000311
Correlation between RedD, sequentially taking 1,2 anddwhether or not less than temperature threshold THREqWhen the distance d is smaller than the distance d, the calculation is stopped, and at the moment, the pixel point distance d is the d-th row data block SqIs denoted as CLq；
(5) Block-by-block long-step selection transient thermal response
(5.1) partitioning the three-dimensional matrix S according to the P pixel value jump points selected in the step (3) in rows and the K pixel value jump points selected in the step (4) in rows to obtain (P +1) x (Q +1) data blocks, wherein the (P) th data block on a row and the (Q) th data block on a column are expressed as Sp,q；
(5.2) for each data block Sp,qSetting a threshold DD, setting the initialization set number g to 1, setting the initialization pixel position i to 1, setting j to 1, and setting the maximum pixel value S (i)zz,jzz,tzz) Corresponding transient thermal response S (i)zz,jzzT), T1, 2, T, stored in the set x (g); then calculate the data block Sp,qTransient thermal response S with middle pixel point at i row and j columnp,q(i, j, T), T1, 2.. T, and the set x (g) are correlated with each other by a degree Rei,jAnd judging:
if Rei,j<DD, g is g +1, and transient thermal response S is carried outp,q(i, j, T), T1, 2, T being stored as a new feature in the set x (g); otherwise, let i equal to i + CLpContinuing to calculate the next transient thermal response Sp,q(i, j, T), T ═ 1, 2., degree of correlation of T with set x (g); if i > Mp,qIf i is equal to i-Mp,q，j＝j+CLqI.e. to the j + CLqColumn is calculated if j > Np,qThen the transient thermal response is selected, wherein Mp,q、Np,qAre respectively a data block Sp,qThe number of rows and columns;
(6) dividing all sets X (g) of all (P +1) x (Q +1) data blocks selected in the step (5), namely transient thermal responses, into L classes by adopting an FCM (fuzzy C-means clustering) algorithm to obtain the class of each transient thermal response;
(7) selecting representatives of each type of transient thermal response based on dynamic multi-target and forming a matrix Y
(7.1) defining a multi-objective function when the i '(i' ═ 1., L) -th transient-like thermal response is represented under the (m +1) -th external environment:
Figure BDA0001940385260000041
wherein the content of the first and second substances,
Figure BDA0001940385260000042
a transient thermal response selected for the i' th class transient thermal response in the m +1 th external environment
Figure BDA0001940385260000043
Is expressed as:
Figure BDA0001940385260000044
Figure BDA0001940385260000045
a transient thermal response selected for the i' th class transient thermal response
Figure BDA0001940385260000046
The calculated Euclidean distance between L-1 classes
Figure BDA0001940385260000047
The components are renumbered and the components are,
Figure BDA0001940385260000048
expressed as:
Figure BDA0001940385260000049
Figure BDA00019403852600000410
for transient thermal response
Figure BDA00019403852600000411
The pixel value at time t i.e. the temperature value,
Figure BDA00019403852600000412
the pixel value of the ith' type transient thermal response cluster center at the t-th moment, namely the temperature value,
Figure BDA00019403852600000413
the pixel value of the j' th class transient thermal response clustering center at the t-th moment is a temperature value;
(7.2), the approximate leading edge solution sets of the multi-target function obtained under the m-1 th environment and the m-th environment are respectively
Figure BDA00019403852600000414
And
Figure BDA00019403852600000415
the corresponding solution sets of population transient thermal response (temperature points) are respectively
Figure BDA00019403852600000416
And
Figure BDA00019403852600000417
the number of which is respectively
Figure BDA00019403852600000418
And
Figure BDA00019403852600000419
after the environment changes, according to the history information of the m-1 th environment and the m-th environment, the transient thermal response of the initialized population of the approximate leading edge solution set under the m +1 th environment is predicted and calculated, and the steps are as follows:
(7.2.1)、
Figure BDA00019403852600000420
is from
Figure BDA00019403852600000421
Randomly selecting N in the solution setETransient thermal response
Figure BDA00019403852600000422
A constructed set of transient thermal responses, N' ═ 1,2ECalculating
Figure BDA00019403852600000423
And (3) concentrating the number W representing the transient thermal response, and obtaining a multidirectional prediction set in the (m +1) th environment:
Figure BDA00019403852600000424
wherein, W1And W2Respectively a lower limit value and an upper limit value of W, and W1＝L+1，W2＝3L，
Figure BDA0001940385260000051
Is an evaluation value of the degree of environmental change at the m-th time, obtained by the following formula:
Figure BDA0001940385260000052
wherein the content of the first and second substances,
Figure BDA0001940385260000053
is from
Figure BDA0001940385260000054
Randomly selecting N in the solution setETransient thermal response
Figure BDA0001940385260000055
A constructed set of transient thermal responses, N' ═ 1,2E；
(7.2.2), selecting W to represent transient thermal response
(7.2.2.1) selection
Figure BDA0001940385260000056
The center of the transient thermal response is collected as the first representative transient thermal response, noted
Figure BDA0001940385260000057
Figure BDA0001940385260000058
Wherein the content of the first and second substances,
Figure BDA0001940385260000059
as a solution set
Figure BDA00019403852600000510
The nth transient thermal response;
(7.2.2.2) subjecting the mixture to a k-means method
Figure BDA00019403852600000511
Clustered into W-1 class with the cluster centers being the remaining W-1 representing transient thermal response
Figure BDA00019403852600000512
A. From
Figure BDA00019403852600000513
Collectively randomly selecting W-1 transient thermal responses as initialization representative transient thermal responses
Figure BDA00019403852600000514
Each representing transient heatResponse to
Figure BDA00019403852600000515
A transient thermal response, as a class, is represented by
Figure BDA00019403852600000516
B. According to the nearest principle, representing transient thermal response
Figure BDA00019403852600000517
Will be provided with
Figure BDA00019403852600000518
Are divided into W-1 classes
Figure BDA00019403852600000519
I.e. transient thermal response
Figure BDA00019403852600000520
If the distance is closest to the representative transient thermal response, the representative transient thermal response is classified as the class of the representative transient thermal response;
C. calculating a representative transient thermal response
Figure BDA00019403852600000521
D. Step B, C is repeated until
Figure BDA00019403852600000522
No longer changed;
(7.2.2.3) representing transient thermal response selected in step (7.2.2.1)
Figure BDA00019403852600000523
Representative transient thermal response clustered with step (7.2.2.2) k-means
Figure BDA00019403852600000524
Merging to form a multi-directional prediction set
Figure BDA00019403852600000525
(7.2.3) PS multidirectional prediction set according to m-1 st and m-th environments
Figure BDA00019403852600000526
And
Figure BDA00019403852600000527
wherein the content of the first and second substances,
Figure BDA00019403852600000528
obtained by the method of steps (7.2.1) and (7.2.2), W' is
Figure BDA00019403852600000529
Collectively representing the number of transient thermal responses;
calculating a predicted direction
Figure BDA0001940385260000061
Figure BDA0001940385260000062
Wherein the content of the first and second substances,
Figure BDA0001940385260000063
is PS multidirectional prediction set
Figure BDA0001940385260000064
Neutralization of
Figure BDA0001940385260000065
The nearest transient thermal response is numbered w';
(7.2.4), when the iteration number g' is 0, the number of the transient thermal responses of the initialized population of the approximate leading edge solution set in the m +1 th environment is NpWherein, in the step (A),
Figure BDA0001940385260000066
the transient thermal response of the initial population is randomly generated in a value range,
Figure BDA0001940385260000067
the transient thermal response of the initial population is obtained by predicting according to the following formula:
Figure BDA0001940385260000068
wherein, wnFor transient thermal response
Figure BDA0001940385260000069
The cluster result
Figure BDA00019403852600000610
The serial number of (a) is included,
Figure BDA00019403852600000611
is a obedient mean of 0 and variance of
Figure BDA00019403852600000612
Normally distributed random number, variance of
Figure BDA00019403852600000613
The calculation formula of (2) is as follows:
Figure BDA00019403852600000614
(7.3) initializing the relevant parameters
The number of initialization iterations g' is 0, and a set of evenly distributed weight vectors
Figure BDA00019403852600000615
Wherein the content of the first and second substances,
Figure BDA00019403852600000616
initializing reference points
Figure BDA00019403852600000617
Is a function of
Figure BDA00019403852600000618
A corresponding reference point;
Figure BDA00019403852600000619
maximum number of iterations g'max；
The evolution speed for initializing each population transient thermal response is
Figure BDA00019403852600000620
Global optimal and local optimal satisfaction of population transient thermal response
Figure BDA00019403852600000621
(7.4) use of
Figure BDA00019403852600000622
Constructing a dynamic objective function fitness value of transient thermal response of each population under a Tchebycheff polymerization method
Figure BDA00019403852600000623
Figure BDA00019403852600000624
(7.5), 1, NP: updating speed according to particle swarm algorithm
Figure BDA00019403852600000625
And population transient thermal response
Figure BDA00019403852600000626
Comparison according to a Multi-objective optimization Algorithm
Figure BDA00019403852600000627
Updating global optimum
Figure BDA0001940385260000071
Local optimization
Figure BDA0001940385260000072
And a reference point
Figure BDA0001940385260000073
From
Figure BDA0001940385260000074
Middle reservation domination
Figure BDA0001940385260000075
Removing all quilt
Figure BDA0001940385260000076
Dominant solution vector if
Figure BDA0001940385260000077
None of the vectors in (1) dominates
Figure BDA0001940385260000078
Will be provided with
Figure BDA0001940385260000079
Adding into
Figure BDA00019403852600000710
N is N +1, N is less than or equal to NPThen g '═ g' + 1;
(7.6) evolution termination judgment: if g 'is less than or equal to g'maxThen repeat step (7.5) if g '> g'maxObtaining the final leading edge approximate solution set of the i' th class temperature transient thermal response
Figure BDA00019403852600000711
(7.7) approximating solution sets from leading edges
Figure BDA00019403852600000712
Selecting a representation of class i' transient thermal responsei'REP, where all the transient thermal responses of L classes represent placement by columns (one column is a temperature value, which is a pixel value at T moments), and form a T × L matrix Y;
(8) starting each frame in the three-dimensional matrix S from a first column, connecting a next column to the tail of a previous column to form a new column, obtaining T-column pixel values corresponding to the T frame, then sequentially placing the T-column pixel values according to time sequence to form an I multiplied by J row and T-column two-dimensional image matrix O, and performing linear transformation on the two-dimensional matrix O by using a matrix Y, namely:
Figure BDA00019403852600000713
a two-dimensional image matrix R is obtained, wherein,
Figure BDA00019403852600000714
is an LxT matrix, is a pseudo-inverse of matrix Y, OTA transpose matrix of the two-dimensional image matrix O, wherein an obtained two-dimensional image matrix R is L rows and I multiplied by J columns;
sequentially intercepting each row of the two-dimensional image matrix R according to J columns, and sequentially placing the intercepted J columns according to the rows to form an I multiplied by J two-dimensional image, so that L I multiplied by J two-dimensional images are obtained by the L rows, all the images contain defect areas, and in order to facilitate defect contour extraction, selecting a two-dimensional image with the maximum difference of pixel values (temperature values) of the defect area and the non-defect area and recording the two-dimensional image as f (x, y);
(9) and performing feature extraction on the two-dimensional image f (x, y) by using a Pulse Coupled Neural Network (PCNN) to obtain defect features:
(9.1) constructing a PCNN network consisting of I multiplied by J neurons, wherein each neuron corresponds to I multiplied by J pixel points of the two-dimensional image f (x, y), and the pixel values of the pixel points in the x-th row and the y-th column are used as external stimuli I of the neural network neurons marked as the x-th row and the y-th columnxySending the image to a PCNN (pulse coupled neural network) to obtain an image segmentation result RE, wherein the RE is a binary matrix;
and (9.2) solving an edge profile of the binary matrix RE to obtain defect characteristics.
The invention aims to realize the following steps:
the invention relates to an infrared thermal image defect feature extraction method based on k-means dynamic multi-target, which selects transient thermal response of pixel points by changing step length of a thermal image sequence, and FCM is adopted for classification to obtain the category of transient thermal response of each pixel point, then, the similarity of the pixel value (temperature value) of each category pixel point and the like pixel point is considered, the difference between the pixel point (temperature point) and the different category pixel points (temperature points) is considered, a corresponding multi-target function is constructed, and simultaneously, after each environment change, a guiding direction is provided for population evolution through a prediction mechanism, the multi-objective optimization algorithm is helped to make quick response to new changes, and obtaining a dimension reduction result of the thermal image sequence through a multi-objective optimization algorithm, and finally extracting features by using a pulse coupling neural network so as to extract defect features of the infrared thermal image. Through the steps, the corresponding (temperature point) of the representative transient heat is accurately selected, the accuracy of defect feature extraction is guaranteed, and meanwhile the calculation consumption for obtaining each category of information representative of the corresponding transient heat in a dynamic environment is reduced.
Meanwhile, the method for extracting the infrared thermal image defect characteristics based on the k-mean dynamic multi-target further has the following beneficial effects:
1. the method adopts a multi-objective optimization method to realize the comprehensive consideration of the difference and the similarity, accurately describes the defect outline, makes up for some defects of the traditional method on dimension reduction processing, and has more representativeness than the defect characteristic extraction based on the algorithm of the difference;
2. the method adopts a multi-direction prediction strategy, introduces a plurality of shapes representing transient thermal response to properly describe the PS (pareto set), and records the distribution condition of the PS in each environment, so as to predict the new position of the PS. After the environment is changed, the new position of the PS is predicted by using the representative transient thermal responses of the previous two environments, and a plurality of new initial population transient thermal responses are generated at the new position, so that the response to the environment change is accelerated.
Drawings
FIG. 1 is a flow chart of an embodiment of the method for extracting defect characteristics of infrared thermal images based on k-means dynamic multi-target according to the present invention;
FIG. 2 is a graph of results of classifying selected transient thermal responses using fuzzy C-means clustering;
FIG. 3 is a graph of transient thermal response of the temperature point of the material itself;
FIG. 4 is a graph of transient thermal response at defect 1 temperature point;
FIG. 5 is a graph of transient thermal response at defect 2 temperature points;
FIG. 6 is a graph of transient thermal response for corresponding temperature points of the material itself, selected based on differences;
FIG. 7 is a graph of transient thermal response for corresponding defect 1 temperature points selected based on variability;
FIG. 8 is a graph of transient thermal response for corresponding defect 2 temperature points selected based on variability;
FIG. 9 is a graph of transient thermal response for a corresponding temperature point of the material itself, selected based on the present invention;
FIG. 10 is a graph of transient thermal response for the corresponding defect 1 temperature point selected based on the present invention;
FIG. 11 is a graph of transient thermal response for corresponding defect 2 temperature points selected based on the present invention;
fig. 12 is a defect feature map extracted based on the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
FIG. 1 is a flowchart of an embodiment of the method for extracting defect features of infrared thermal images based on k-means dynamic multi-target.
In this embodiment, as shown in fig. 1, the method for extracting defect features of infrared thermal image based on k-means dynamic multiple targets of the present invention includes the following steps:
step S1: the sequence of thermal images is represented as a three-dimensional matrix
The thermal image sequence acquired by the thermal infrared imager is represented by a three-dimensional matrix S, wherein elements S (i, j, t) represent pixel values of ith row and jth column of t frames of thermal images of the thermal image sequence.
Step S2: select the maximum pixel value
Selecting the maximum pixel value S (i) from the three-dimensional matrix Szz,jzz,tzz) Wherein i iszz、jzzAnd tzzRespectively representing the row number of the row where the pixel point with the maximum pixel value is located, the column number of the column where the pixel point is located and the frame number of the frame where the pixel point is located.
Step S3: dividing a row of data blocks and calculating the row step length
T for three-dimensional matrix SzzFrame, select jthzzSelecting P pixel value jumping points according to the change of pixel values (namely temperature values), wherein the jumping points are positioned between two jumping pixel value pixel points, and dividing the three-dimensional matrix S according to rows by the jumping points to obtain P +1 row data blocks;
at the p-th line data block SpWhere (P ═ 1, 2., P +1), the maximum pixel value is found, denoted as
Figure BDA0001940385260000091
Wherein the content of the first and second substances,
Figure BDA0001940385260000092
respectively representing the p-th line data block SpThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure BDA0001940385260000093
Corresponding transient thermal response is
Figure BDA0001940385260000094
T is the total number of S frames of the three-dimensional matrix;
setting a p-th line data block SpHas a temperature threshold of THREpCalculating transient thermal response
Figure BDA0001940385260000095
From the maximum pixel value, i.e. the maximum value of the temperature
Figure BDA0001940385260000096
Transient thermal response corresponding to pixel values of pixels in near and far rows where pixels are located
Figure BDA0001940385260000097
Correlation between RebB, sequentially taking 1,2 and judging the correlation RebWhether or not less than temperature threshold THREpWhen the distance b is less than the p-th row data block line data block S, the calculation is stopped, and at the moment, the pixel point distance b ispLine step length of (1), noted as CLp。
Step S4: dividing column data block and calculating column step length
T for three-dimensional matrix SzzFrame, select the ithzzIn the row, Q pixel value jump points are selected according to the change of pixel values (namely temperature values), the jump points are positioned between two jump pixel value pixel points, and the jump points divide the three-dimensional matrix S according to columns to obtain Q +1 column data blocks;
in the q column data block SqWhere (Q ═ 1, 2., Q +1), the maximum pixel value is found, denoted as
Figure BDA0001940385260000101
Wherein the content of the first and second substances,
Figure BDA0001940385260000102
respectively representing the q-th column data block SqThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure BDA0001940385260000103
Corresponding transient thermal response is
Figure BDA0001940385260000104
T is the total number of S frames of the three-dimensional matrix;
setting a qth column data block SqHas a temperature threshold of THREqCalculating transient thermal response
Figure BDA0001940385260000105
From the maximum pixel value of the distanceMaximum value of temperature
Figure BDA0001940385260000106
Transient thermal response corresponding to pixel values of pixels from near to far in row of pixel
Figure BDA0001940385260000107
Correlation between RedD, sequentially taking 1,2 anddwhether or not less than temperature threshold THREqWhen the distance d is smaller than the distance d, the calculation is stopped, and at the moment, the pixel point distance d is the d-th row data block SqIs denoted as CLq。
Step S5: block and step selection transient thermal response
Step S5.1: partitioning the three-dimensional matrix S according to the P pixel value jump points selected in the step S3 by columns and the Q pixel value jump points selected in the step S4 by rows to obtain (P +1) × (Q +1) data blocks, wherein the (P) th data block on a row and the (Q) th data block on a column are represented as Sp,q；
Step S5.2: for each data block Sp,qSetting a threshold DD, setting the initialization set number g to 1, setting the initialization pixel position i to 1, setting j to 1, and setting the maximum pixel value S (i)zz,jzz,tzz) Corresponding transient thermal response S (i)zz,jzzT), T1, 2, T, stored in the set x (g); then calculate the data block Sp,qTransient thermal response S with middle pixel point at i row and j columnp,q(i, j, T), T1, 2.. T, and the set x (g) are correlated with each other by a degree Rei,jAnd judging:
if Rei,j<DD, g is g +1, and transient thermal response S is carried outp,q(i, j, T), T1, 2, T being stored as a new feature in the set x (g); otherwise, let i equal to i + CLpContinuing to calculate the next transient thermal response Sp,q(i, j, T), T ═ 1, 2., degree of correlation of T with set x (g); if i > Mp,qIf i is equal to i-Mp,q，j＝j+CLqI.e. to the j + CLqColumn is calculated if j > Np,qThen the transient thermal response is selected, wherein Mp,q、Np,qAre respectively a data block Sp,qThe number of rows and columns.
Step S6: classifying selected transient thermal responses using fuzzy C-means clustering
All sets x (g) of all (P +1) × (Q +1) data blocks selected in step S5, i.e., transient thermal responses, are classified into L classes by using an FCM (fuzzy C-means clustering) algorithm, and the class to which each transient thermal response belongs is obtained.
In this embodiment, specifically, the following steps are included:
step S6.1: setting a clustering number L, setting an initial iteration number c to be 0, and setting a termination iteration condition threshold epsilon;
step S6.2: using formulas
Figure BDA00019403852600001111
Calculating a membership matrix U;
wherein i ═ 1,2, …, L, c ∈ L,n'dk'＝||xk'-i'V||，n'＝i',j'，n'dk'representing the k 'th pixel point and the i' th cluster centeri'Euclidean distance of V, xk'Representing the coordinates of the kth pixel point; τ is a constant;i'uk'expressing the degree of the k 'th pixel point belonging to the i' th class;
step S6.3: updating cluster centersi'V
Figure BDA0001940385260000112
Wherein the content of the first and second substances,
Figure BDA0001940385260000113
expressing the thermal response value of the k' th pixel point;
step S6.4: if the iteration times reach the maximum value L or the absolute value of the difference between the clustering centers of the two times is less than epsilon, finishing the algorithm, outputting a membership matrix U and a clustering center V, and then entering the step S6.5; otherwise, let c be c +1, return to step S6.2;
step S6.5: using membership maximizationDefuzzification is carried out on all pixel points according to the criterion to obtain the category of each pixel point, namely Mk'＝argi'max(i'uk')。
Step S7: selecting representatives of each type of transient thermal response based on dynamic multi-target and forming a matrix Y
Step S7.1: in the case of the (m +1) th external environment, when the i '(i' ═ 1., L) th class transient thermal response is selected, a multi-objective function is defined:
Figure BDA0001940385260000114
wherein the content of the first and second substances,
Figure BDA0001940385260000115
a transient thermal response selected for the i' th class transient thermal response in the m +1 th external environment
Figure BDA0001940385260000116
Is expressed as:
Figure BDA0001940385260000117
Figure BDA0001940385260000118
a transient thermal response selected for the i' th class transient thermal response
Figure BDA0001940385260000119
The calculated Euclidean distance between L-1 classes
Figure BDA00019403852600001110
The components are renumbered and the components are,
Figure BDA0001940385260000121
expressed as:
Figure BDA0001940385260000122
Figure BDA0001940385260000123
for transient thermal response
Figure BDA0001940385260000124
The pixel value at time t i.e. the temperature value,
Figure BDA0001940385260000125
the pixel value of the ith' type transient thermal response cluster center at the t-th moment, namely the temperature value,
Figure BDA0001940385260000126
the pixel value of the j' th class transient thermal response clustering center at the t-th moment is a temperature value;
step S7.2: the multi-target function approximation leading edge solution sets obtained under the m-1 th environment and the m-th environment are respectively
Figure BDA0001940385260000127
And
Figure BDA0001940385260000128
the corresponding solution sets of population transient thermal response (temperature points) are respectively
Figure BDA0001940385260000129
And
Figure BDA00019403852600001210
the number of which is respectively
Figure BDA00019403852600001211
And
Figure BDA00019403852600001212
after the environment changes, according to the history information of the m-1 th environment and the m-1 th environment, the transient thermal response of the initialized population of the approximate leading edge solution set under the m +1 th environment is predicted and calculated, and the steps are as followsThe following:
step S7.2.1:
Figure BDA00019403852600001213
is from
Figure BDA00019403852600001214
Randomly selecting N in the solution setETransient thermal response
Figure BDA00019403852600001215
A constructed set of transient thermal responses, N' ═ 1,2ECalculating
Figure BDA00019403852600001216
And (3) concentrating the number W representing the transient thermal response, and obtaining a multidirectional prediction set in the (m +1) th environment:
Figure BDA00019403852600001217
wherein, W1And W2Respectively a lower limit value and an upper limit value of W, and W1＝L+1，W2＝3L，
Figure BDA00019403852600001218
Is an evaluation value of the degree of environmental change at the m-th time, obtained from the following equation:
Figure BDA00019403852600001219
wherein the content of the first and second substances,
Figure BDA00019403852600001220
is from
Figure BDA00019403852600001221
Randomly selecting N in the solution setETransient thermal response
Figure BDA00019403852600001222
Is composed ofSet of transient thermal responses, N' ═ 1,2E；
Step S7.2.2: selecting W to represent transient thermal response
Step S7.2.2.1: selecting
Figure BDA00019403852600001223
The center of the transient thermal response is collected as the first representative transient thermal response, noted
Figure BDA00019403852600001224
Figure BDA00019403852600001225
Wherein the content of the first and second substances,
Figure BDA00019403852600001226
as a solution set
Figure BDA00019403852600001227
The nth transient thermal response;
step S7.2.2.2: by the k-means method
Figure BDA00019403852600001228
Clustered into W-1 class with the cluster centers being the remaining W-1 representing transient thermal response
Figure BDA00019403852600001229
A. From
Figure BDA0001940385260000131
Collectively randomly selecting W-1 transient thermal responses as initialization representative transient thermal responses
Figure BDA0001940385260000132
Each representing a transient thermal response
Figure BDA0001940385260000133
A transient thermal response as a classIs shown as
Figure BDA0001940385260000134
B. According to the nearest principle, representing transient thermal response
Figure BDA0001940385260000135
Will be provided with
Figure BDA0001940385260000136
Are divided into W-1 classes
Figure BDA0001940385260000137
I.e. transient thermal response
Figure BDA0001940385260000138
If the distance is closest to the representative transient thermal response, the representative transient thermal response is classified as the class of the representative transient thermal response;
C. calculating a representative transient thermal response
Figure BDA0001940385260000139
D. Step B, C is repeated until
Figure BDA00019403852600001310
No longer changed;
step S7.2.2.3: representing the transient thermal response selected at step S7.2.2.1
Figure BDA00019403852600001311
Representative transient thermal response clustered with step S7.2.2.1k-means
Figure BDA00019403852600001312
Merging to form a multi-directional prediction set
Figure BDA00019403852600001313
Step S7.2.3: PS multidirectional prediction set according to m-1 th and m-th environments
Figure BDA00019403852600001314
And
Figure BDA00019403852600001315
wherein the content of the first and second substances,
Figure BDA00019403852600001316
obtained by the process of steps S7.2.1, S7.2.2, W' is
Figure BDA00019403852600001317
Collectively representing the number of transient thermal responses;
calculating a predicted direction
Figure BDA00019403852600001318
Figure BDA00019403852600001319
Wherein the content of the first and second substances,
Figure BDA00019403852600001320
is PS multidirectional prediction set
Figure BDA00019403852600001321
Neutralization of
Figure BDA00019403852600001322
The nearest transient thermal response is numbered w';
step S7.2.4: when the iteration number g' is 0, the number of transient thermal responses of the initialized population of the approximate leading edge solution set under the m +1 th environment is NpWherein, in the step (A),
Figure BDA00019403852600001323
the transient thermal response of the initial population is randomly generated in a value range,
Figure BDA00019403852600001324
the transient thermal response of the initial population is obtained by predicting according to the following formula:
Figure BDA00019403852600001325
wherein, wnFor transient thermal response
Figure BDA00019403852600001326
The cluster result
Figure BDA00019403852600001327
The serial number of (a) is included,
Figure BDA00019403852600001328
is a obedient mean of 0 and variance of
Figure BDA00019403852600001329
Normally distributed random number, variance of
Figure BDA00019403852600001330
The calculation formula of (2) is as follows:
Figure BDA00019403852600001331
according to the invention, the transient thermal response of the initialized population of the approximate leading edge solution set in the (m +1) th environment is obtained according to the historical information in the previous environment, so that a guide direction is provided for population evolution, and the rapid response of a multi-objective optimization algorithm to new changes is helped.
S7.3: initializing relevant parameters
The number of initialization iterations g' is 0, and a set of evenly distributed weight vectors
Figure BDA0001940385260000141
Wherein the content of the first and second substances,
Figure BDA0001940385260000142
initializing reference points
Figure BDA0001940385260000143
Is a function of
Figure BDA0001940385260000144
A corresponding reference point;
Figure BDA0001940385260000145
maximum number of iterations g'max；
The evolution speed for initializing each population transient thermal response is
Figure BDA0001940385260000146
Global optimal and local optimal satisfaction of population transient thermal response
Figure BDA0001940385260000147
S7.4: by using
Figure BDA0001940385260000148
Constructing a dynamic objective function fitness value of transient thermal response of each population under a Tchebycheff polymerization method
Figure BDA0001940385260000149
Figure BDA00019403852600001410
S7.5: n is 1P: updating speed according to particle swarm algorithm
Figure BDA00019403852600001411
And population transient thermal response
Figure BDA00019403852600001412
Comparison according to a Multi-objective optimization Algorithm
Figure BDA00019403852600001413
Updating global optimum
Figure BDA00019403852600001414
Local optimization
Figure BDA00019403852600001415
And a reference point
Figure BDA00019403852600001416
From
Figure BDA00019403852600001417
Middle reservation domination
Figure BDA00019403852600001418
Removing all quilt
Figure BDA00019403852600001419
Dominant solution vector if
Figure BDA00019403852600001420
None of the vectors in (1) dominates
Figure BDA00019403852600001421
Will be provided with
Figure BDA00019403852600001422
Adding into
Figure BDA00019403852600001423
N is N +1, N is less than or equal to NPThen g '═ g' + 1;
s7.6: judging the evolution termination: if g 'is less than or equal to g'maxThen step S7.5 is repeated if g '> g'maxObtaining the final leading edge approximate solution set of the i' th class temperature transient thermal response
Figure BDA00019403852600001424
S7.7: approximate solution set from leading edge
Figure BDA00019403852600001425
Selecting a representation of class i' transient thermal responsei' REP, all L classesThe transient thermal response of (a) represents the placement in columns (one column is the pixel value at T moments, i.e. the temperature value), forming a matrix Y of T × L;
step S8: and (3) converting the three-dimensional matrix S into a two-dimensional matrix, and performing linear transformation on the two-dimensional matrix S by using the matrix Y to obtain a two-dimensional image matrix R and a two-dimensional image f (x, Y) with the maximum difference of pixel values (temperature values):
starting each frame in the three-dimensional matrix S from a first column, connecting a next column to the end of a previous column to form a new column, obtaining T-column pixel values corresponding to the T frame, then sequentially placing the T-column pixel values according to time sequence to form an I × J row and T-column two-dimensional image matrix O, and performing linear transformation on the two-dimensional matrix O by using a matrix Y, namely:
Figure BDA0001940385260000151
a two-dimensional image matrix R is obtained, wherein,
Figure BDA0001940385260000152
is an LxT matrix, is a pseudo-inverse of matrix Y, OTA transpose matrix of the two-dimensional image matrix O, wherein an obtained two-dimensional image matrix R is L rows and I multiplied by J columns;
and intercepting each row of the two-dimensional image matrix R in turn according to J columns, and sequentially placing the intercepted J columns according to the rows to form an I multiplied by J two-dimensional image, so that L I multiplied by J two-dimensional images are obtained by L rows, all the images contain defect areas, and in order to facilitate defect contour extraction, selecting the two-dimensional image with the maximum difference of pixel values (temperature values) of the defect area and the non-defect area, and recording the two-dimensional image as f (x, y).
Step S9: performing feature extraction on the two-dimensional image f (x, y) by using a Pulse Coupled Neural Network (PCNN) to obtain defect features
Step S9.1: constructing a PCNN network consisting of I multiplied by J neurons, wherein each neuron corresponds to I multiplied by J pixel points of a two-dimensional image f (x, y), and taking the pixel values of the pixel points in the x-th row and the y-th column as labels to be the external stimulus I of the neural network neurons in the x-th row and the y-th columnxySending the image to a PCNN (pulse coupled neural network) to obtain an image segmentation result RE, wherein the RE is a binary matrix;
step S9.2: and solving the edge contour of the binary matrix RE to obtain the defect characteristics.
Examples of the invention
In the present embodiment, there are two kinds of defects on the test piece, i.e., defect 1 filled with no material and defect 2 filled with a poor thermal conductive material.
In this embodiment, a result graph obtained by classifying the selected transient thermal response by using the fuzzy C-means clustering is shown in fig. 2.
Three known temperature points, namely transient thermal response curves of a material temperature point, a defect 1 temperature point and a defect 2 temperature point are directly extracted from a thermal image sequence of the test piece and are respectively recorded asBacPOINT、Def1POINT andDef2POINT, as shown in fig. 3, 4, 5.
Three transient thermal response representations are obtained by using the existing method for selecting the transient thermal response representation based on difference:ANFCM7、BNFCM4andcNFCM21they correspond to the temperature point of the material itself, the temperature point of the defect 1 and the temperature point of the defect 2, respectively, and the curves are shown in fig. 6, 7 and 8.
By using the method for dynamically selecting the transient thermal response representatives through multi-objective optimization, three transient thermal response representatives are obtained:ANFCM13、BNFCM10andcNFCM24they correspond to the temperature point of the material itself, the temperature point of the defect 1 and the temperature point of the defect 2, respectively, and the curves are shown in fig. 9, 10 and 11.
From the transient thermal response curve: the temperature point of the defect 1 has a clear descending trend, and the amplitude temperature of the temperature point of the defect 2 is the lowest. Compared with the three characteristics, the temperature point of the defect 1 releases heat most quickly, and the temperature point of the defect 2 releases heat most slowly.
The correlation between the transient thermal response curves under the two methods and the corresponding transient thermal response curves extracted directly from the thermographic sequence is shown in table 1.
Temperature point of itself Temperature point of defect 1 Temperature point of defect 2
Based on a difference method 0.9979 0.9817 0.9970
The invention 0.9985 0.9993 0.9973
TABLE 1
From table 1, it can be seen that the transient thermal response curves selected by the method of the present invention are more correlated.
In the present embodiment, the defect features extracted are as shown in fig. 12.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (1)

1. A method for extracting defect characteristics of infrared thermal images based on k-means dynamic multi-target is characterized by comprising the following steps:
(1) representing a thermal image sequence acquired by the thermal infrared imager by using a three-dimensional matrix S, wherein elements S (i, j, t) represent pixel values of an ith row and a jth column of a t-frame thermal image of the thermal image sequence;
(2) selecting the maximum pixel value S (i) from the three-dimensional matrix Szz,jzz,tzz) Wherein i iszz、jzzAnd tzzRespectively representing the row number of the row where the pixel point with the maximum pixel value is located, the column number of the column where the pixel point is located and the frame number of the frame where the pixel point is located;
(3) t for three-dimensional matrix SzzFrame, select jthzzSelecting P pixel value jumping points according to the change of the pixel values, wherein the jumping points are positioned between two jumping pixel value pixel points, and dividing the three-dimensional matrix S according to rows by the jumping points to obtain P +1 row data blocks;
at the p-th line data block SpWhere (P ═ 1, 2., P +1), the maximum pixel value is found, denoted as
Figure FDA0002902666680000011
Wherein the content of the first and second substances,
Figure FDA0002902666680000012
respectively representing the p-th line data block SpThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure FDA0002902666680000013
Corresponding transient thermal response is
Figure FDA0002902666680000014
T is the total number of S frames of the three-dimensional matrix;
setting a p-th line data block SpHas a temperature threshold of THREpCalculating transient thermal response
Figure FDA0002902666680000015
From the maximum pixel value, i.e. the maximum value of the temperature
Figure FDA0002902666680000016
Transient thermal response corresponding to pixel values of pixels in near and far rows where pixels are located
Figure FDA0002902666680000017
Correlation between RebB, sequentially taking 1,2 and judging the correlation RebWhether or not less than temperature threshold THREpWhen the distance b is less than the p-th row data block line data block S, the calculation is stopped, and at the moment, the pixel point distance b ispLine step length of (1), noted as CLp；
(4) T for three-dimensional matrix SzzFrame, select the ithzzIn the row, Q pixel value jumping points are selected according to the change of the pixel values, the jumping points are positioned between two jumping pixel value pixel points, and the three-dimensional matrix S is divided by the jumping points in columns to obtain Q +1 column data blocks;
in the q column data block SqWhere (Q ═ 1, 2., Q +1), the maximum pixel value is found, denoted as
Figure FDA0002902666680000018
Wherein the content of the first and second substances,
Figure FDA0002902666680000019
respectively representing the q-th column data block SqThe number of rows of the row where the pixel point with the middle and maximum pixel values is located, the number of columns of the column where the pixel point with the middle and maximum pixel values is located and the frame number of the frame where the pixel point with the middle and maximum pixel values is located, the maximum pixel value
Figure FDA00029026666800000110
Corresponding transient thermal response is
Figure FDA00029026666800000111
T is the total number of S frames of the three-dimensional matrix;
setting a qth column data block SqHas a temperature threshold of THREqCalculating transient thermal response
Figure FDA00029026666800000112
From the maximum pixel value, i.e. the maximum value of the temperature
Figure FDA00029026666800000113
Transient thermal response corresponding to pixel values of pixels from near to far in row of pixel
Figure FDA00029026666800000114
Correlation between RedD, sequentially taking 1,2 anddwhether or not less than temperature threshold THREqWhen the distance d is smaller than the distance d, the calculation is stopped, and at the moment, the pixel point distance d is the d-th row data block SqIs denoted as CLq；
(5) Block-by-block long-step selection transient thermal response
(5.1) partitioning the three-dimensional matrix S according to the P pixel value jump points selected in the step (3) in rows and the K pixel value jump points selected in the step (4) in rows to obtain (P +1) x (Q +1) data blocks, wherein the (P) th data block on a row and the (Q) th data block on a column are expressed as Sp,q；
(5.2) for each data block Sp,qSetting a threshold DD, setting the initialization set number g to 1, setting the initialization pixel position i to 1, setting j to 1, and setting the maximum pixel value S (i)zz,jzz,tzz) Corresponding transient thermal response S (i)zz,jzzT), T1, 2, T, stored in the set x (g); then calculate the data block Sp,qTransient thermal response S with middle pixel point at i row and j columnp,q(i, j, T), T1, 2.. T, and the set x (g) are correlated with each other by a degree Rei,jAnd judging:
if Rei,j<DD, g is g +1, and transient thermal response S is carried outp,q(i, j, T), T1, 2, T being stored as a new feature in the set x (g); otherwise, let i equal to i + CLpContinuing to calculate the next transient thermal response Sp,q(i, j, T), T ═ 1, 2., degree of correlation of T with set x (g); if i > Mp,qIf i is equal to i-Mp,q，j＝j+CLqI.e. to the j + CLqIs in line withCalculating if j > Np,qThen the transient thermal response is selected, wherein Mp,q、Np,qAre respectively a data block Sp,qThe number of rows and columns;
(6) dividing all sets X (g) of all (P +1) x (Q +1) data blocks selected in the step (5), namely transient thermal responses, into L classes by adopting an FCM (fuzzy C-means clustering) algorithm to obtain the class of each transient thermal response;
(7) selecting representatives of each type of transient thermal response based on dynamic multi-target and forming a matrix Y
(7.1) defining a multi-objective function when the i '(i' ═ 1., L) -th transient-like thermal response is represented under the (m +1) -th external environment:
Figure FDA0002902666680000021
wherein the content of the first and second substances,
Figure FDA0002902666680000022
a transient thermal response selected for the i' th class transient thermal response in the m +1 th external environment
Figure FDA0002902666680000023
Is expressed as:
Figure FDA0002902666680000024
Figure FDA0002902666680000025
a transient thermal response selected for the i' th class transient thermal response
Figure FDA0002902666680000026
The calculated Euclidean distance between L-1 classes
Figure FDA0002902666680000027
The components are renumbered and the components are,
Figure FDA0002902666680000028
expressed as:
Figure FDA0002902666680000031
Figure FDA0002902666680000032
for transient thermal response
Figure FDA0002902666680000033
The pixel value at time t i.e. the temperature value,
Figure FDA0002902666680000034
the pixel value of the ith' type transient thermal response cluster center at the t-th moment, namely the temperature value,
Figure FDA0002902666680000035
the pixel value of the j' th class transient thermal response clustering center at the t-th moment is a temperature value;
(7.2), the approximate leading edge solution sets of the multi-target function obtained under the m-1 th environment and the m-th environment are respectively
Figure FDA0002902666680000036
And
Figure FDA0002902666680000037
the corresponding population transient thermal response solution sets are respectively
Figure FDA0002902666680000038
And
Figure FDA0002902666680000039
number of themAre respectively as
Figure FDA00029026666800000310
And
Figure FDA00029026666800000311
after the environment changes, according to the history information of the m-1 th environment and the m-th environment, the transient thermal response of the initialized population of the approximate leading edge solution set under the m +1 th environment is predicted and calculated, and the steps are as follows:
(7.2.1)、
Figure FDA00029026666800000312
is from
Figure FDA00029026666800000313
Randomly selecting N in the solution setETransient thermal response
Figure FDA00029026666800000314
A constructed set of transient thermal responses, N' ═ 1,2ECalculating
Figure FDA00029026666800000315
And (3) concentrating the number W representing the transient thermal response, and obtaining a multidirectional prediction set in the (m +1) th environment:
Figure FDA00029026666800000316
wherein, W1And W2Respectively a lower limit value and an upper limit value of W, and W1＝L+1，W2＝3L，
Figure FDA00029026666800000317
Is an evaluation value of the degree of environmental change at the m-th time, obtained by the following formula:
Figure FDA00029026666800000318
wherein the content of the first and second substances,
Figure FDA00029026666800000319
is from
Figure FDA00029026666800000320
Randomly selecting N in the solution setETransient thermal response
Figure FDA00029026666800000321
A constructed set of transient thermal responses, N' ═ 1,2E；
(7.2.2), selecting W to represent transient thermal response
(7.2.2.1) selection
Figure FDA00029026666800000322
The center of the transient thermal response is collected as the first representative transient thermal response, noted
Figure FDA00029026666800000323
Figure FDA00029026666800000324
Wherein the content of the first and second substances,
Figure FDA00029026666800000325
as a solution set
Figure FDA00029026666800000326
The nth transient thermal response;
(7.2.2.2) subjecting the mixture to a k-means method
Figure FDA00029026666800000327
Clustered into W-1 class with the cluster centers being the remaining W-1 representing transient thermal response
Figure FDA00029026666800000328
A. From
Figure FDA00029026666800000329
Collectively randomly selecting W-1 transient thermal responses as initialization representative transient thermal responses
Figure FDA00029026666800000330
Each representing a transient thermal response
Figure FDA00029026666800000331
A transient thermal response, as a class, is represented by
Figure FDA0002902666680000041
B. According to the nearest principle, representing transient thermal response
Figure FDA0002902666680000042
Will be provided with
Figure FDA0002902666680000043
Are divided into W-1 classes
Figure FDA0002902666680000044
I.e. transient thermal response
Figure FDA0002902666680000045
If the distance is closest to the representative transient thermal response, the representative transient thermal response is classified as the class of the representative transient thermal response;
C. calculating a representative transient thermal response
Figure FDA0002902666680000046
D. Step B, C is repeated until
Figure FDA0002902666680000047
No longer changed;
(7.2.2.3) representing transient thermal response selected in step (7.2.2.1)
Figure FDA0002902666680000048
Representative transient thermal response clustered with step (7.2.2.2) k-means
Figure FDA0002902666680000049
Merging to form a multi-directional prediction set
Figure FDA00029026666800000410
(7.2.3) PS multidirectional prediction set according to m-1 st and m-th environments
Figure FDA00029026666800000411
And
Figure FDA00029026666800000412
wherein the content of the first and second substances,
Figure FDA00029026666800000413
obtained by the method of steps (7.2.1) and (7.2.2), W' is
Figure FDA00029026666800000414
Collectively representing the number of transient thermal responses;
calculating a predicted direction
Figure FDA00029026666800000415
Figure FDA00029026666800000416
Wherein the content of the first and second substances,
Figure FDA00029026666800000417
is PS multidirectional prediction set
Figure FDA00029026666800000418
Neutralization of
Figure FDA00029026666800000419
The nearest transient thermal response is numbered w';
(7.2.4), when the iteration number g' is 0, the number of the transient thermal responses of the initialized population of the approximate leading edge solution set in the m +1 th environment is NpWherein, in the step (A),
Figure FDA00029026666800000420
the transient thermal response of the initial population is randomly generated in a value range,
Figure FDA00029026666800000421
the transient thermal response of the initial population is obtained by predicting according to the following formula:
Figure FDA00029026666800000422
wherein, wnFor transient thermal response
Figure FDA00029026666800000423
The cluster result
Figure FDA00029026666800000424
The serial number of (a) is included,
Figure FDA00029026666800000425
is a obedient mean of 0 and variance of
Figure FDA00029026666800000426
Normally distributed random number, variance of
Figure FDA00029026666800000427
The calculation formula of (2) is as follows:
Figure FDA00029026666800000428
(7.3) initializing the relevant parameters
The number of initialization iterations g' is 0, and a set of evenly distributed weight vectors
Figure FDA00029026666800000429
Wherein the content of the first and second substances,
Figure FDA00029026666800000430
initializing reference points
Figure FDA0002902666680000051
Is a function of
Figure FDA0002902666680000052
A corresponding reference point;
Figure FDA0002902666680000053
maximum number of iterations g'max；
The evolution speed for initializing each population transient thermal response is
Figure FDA0002902666680000054
Global optimal and local optimal satisfaction of population transient thermal response
Figure FDA0002902666680000055
(7.4) use of
Figure FDA0002902666680000056
Constructing a dynamic objective function fitness value of transient thermal response of each population under a Tchebycheff polymerization method
Figure FDA0002902666680000057
Figure FDA0002902666680000058
(7.5), 1, NP: updating speed according to particle swarm algorithm
Figure FDA0002902666680000059
And population transient thermal response
Figure FDA00029026666800000510
Comparison according to a Multi-objective optimization Algorithm
Figure FDA00029026666800000511
Updating global optimum
Figure FDA00029026666800000512
Local optimization
Figure FDA00029026666800000513
And a reference point
Figure FDA00029026666800000514
From
Figure FDA00029026666800000515
Middle reservation domination
Figure FDA00029026666800000516
Removing all quilt
Figure FDA00029026666800000517
Dominant solution vector if
Figure FDA00029026666800000518
None of the vectors in (1) dominates
Figure FDA00029026666800000519
Will be provided with
Figure FDA00029026666800000520
Adding into
Figure FDA00029026666800000521
N is N +1, N is less than or equal to NPThen g '═ g' + 1;
(7.6) evolution termination judgment: if g 'is less than or equal to g'maxThen repeat step (7.5) if g '> g'maxObtaining the final leading edge approximate solution set of the i' th class temperature transient thermal response
Figure FDA00029026666800000522
(7.7) approximating solution sets from leading edges
Figure FDA00029026666800000523
Selecting a representation of class i' transient thermal responsei'REP, placing all transient thermal response representatives of L types according to columns to form a T multiplied by L matrix Y;
(8) starting each frame in the three-dimensional matrix S from a first column, connecting a next column to the tail of a previous column to form a new column, obtaining T-column pixel values corresponding to the T frame, then sequentially placing the T-column pixel values according to time sequence to form an I multiplied by J row and T-column two-dimensional image matrix O, and performing linear transformation on the two-dimensional matrix O by using a matrix Y, namely:
Figure FDA00029026666800000524
a two-dimensional image matrix R is obtained, wherein,
Figure FDA00029026666800000525
is an LxT matrix, is a pseudo-inverse of matrix Y, OTA transpose matrix of the two-dimensional image matrix O, wherein an obtained two-dimensional image matrix R is L rows and I multiplied by J columns;
sequentially intercepting each row of the two-dimensional image matrix R according to J columns, and sequentially placing the intercepted J columns according to the rows to form an I multiplied by J two-dimensional image, so that L rows obtain L I multiplied by J two-dimensional images, wherein the images all contain defect areas, and in order to facilitate defect contour extraction, a two-dimensional image with the largest pixel value difference between the defect area and the non-defect area is selected and recorded as f (x, y);
(9) and performing feature extraction on the two-dimensional image f (x, y) by using a Pulse Coupled Neural Network (PCNN) to obtain defect features:
(9.1) constructing a PCNN network consisting of I multiplied by J neurons, wherein each neuron corresponds to I multiplied by J pixel points of the two-dimensional image f (x, y), and the pixel values of the pixel points in the x-th row and the y-th column are used as external stimuli I of the neural network neurons marked as the x-th row and the y-th columnxySending the image to a PCNN (pulse coupled neural network) to obtain an image segmentation result RE, wherein the RE is a binary matrix;
and (9.2) solving an edge profile of the binary matrix RE to obtain defect characteristics.
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