CN114841921B - Thermal imaging uneven temperature background acquisition method and defect detection method - Google Patents
Thermal imaging uneven temperature background acquisition method and defect detection method Download PDFInfo
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Abstract
The method comprises the steps of analyzing each row of pixel points of a thermal imaging image to obtain the uneven temperature background of each row of pixel points; and then combining the uneven temperature backgrounds of all rows of pixel points to form the uneven temperature background of the whole thermal imaging image. According to the invention, firstly, the non-defective pixel points are selected according to the experience rule, and then the non-uniform temperature background of the thermal imaging image is constructed through the non-defective pixel points, so that the accurate calculation of the non-uniform temperature background of the thermal imaging image is realized, and a foundation is laid for eliminating the non-uniform temperature background.
Description
Technical Field
The invention relates to the technical field of thermal imaging defect detection, in particular to a thermal imaging uneven temperature background acquisition method and a defect detection method.
Background
With rapid development and progress of modern material technology and science, industries such as aviation industry, military machinery, petroleum and natural gas are increasingly important in our lives. However, many important structures and materials in the production and equipment operation processes in these industries often have problems such as cracks and damages which are difficult to predict. These defects will significantly reduce the strength and performance of the material under load, eventually leading to structural damage, reduced life of the material, and even serious safety accidents. Therefore, nondestructive testing technology is emerging as an important means of ensuring safe operation and reliability of various types of equipment and products.
Infrared thermal imaging technology is widely studied as an emerging nondestructive detection technology, and the infrared thermal imaging technology heats an object through external means such as optical excitation, vortex excitation and the like, the existence of defects prevents heat from spreading, and then a thermal image sequence of the surface of the object is acquired and analyzed to detect whether the object has defects such as cracks, layering, corrosion and the like. In particular, infrared thermal imaging technology has many advantages over other non-destructive detection technologies, for example infrared thermal imaging is a non-contact technology, which means that it does not affect the target or interfere with its operation in any way, and can safely monitor extremely high temperature or dangerous objects. Most importantly, the infrared thermal imaging technology requires only simple and relatively inexpensive instrumentation and is simple, with the training time of the infrared thermal imaging equipment inspector being much less than that of other non-destructive testing technologies. However, when the infrared thermal imaging technology is used to detect defects, the heat source position, equipment aging, uneven surface optical characteristics and other factors can cause uneven heating, and the uneven heating is difficult to avoid, so that the detection result is seriously affected and the image quality is reduced.
Disclosure of Invention
In order to solve the adverse effect of uneven heating on detection precision in the existing infrared thermal imaging technology, the invention provides a thermal imaging uneven temperature background acquisition method and a defect detection method.
The method for acquiring the thermal imaging uneven temperature background provided by the invention realizes accurate analysis of the uneven temperature background.
The method comprises the steps of analyzing each row of pixel points of a thermal imaging image to obtain the uneven temperature background of each row of pixel points; combining the uneven temperature backgrounds of all rows of pixel points to form an uneven temperature background of the whole thermal imaging image;
the method for acquiring the uneven temperature background of each row of pixel points comprises the following steps:
s11, setting a non-defect pixel point set and a temperature fitting model, wherein the temperature fitting model is as follows:
T(i)＝f θ (i)
wherein, T (i) is the measured temperature value of the ith pixel point of the row, and f represents the mapping relation between the measured temperature value T (i) and the pixel point ordering i; θ represents model parameters of the temperature fitting model;
s12, let i' be any value in a plurality (2, 3, 4 … …, N-1);
s13, substituting temperature data ((1, T (1)), (N, T (N)), (i ', T (i ')) into a temperature fitting model, solving a model parameter theta=θ1, (i ', T (i ')) represents a measured temperature value of the ith pixel point of the row as T (i '),; N is the number of pixel points of each row of the thermal imaging image;
s4, substituting θ=θ1 into the temperature fitting model to obtain a temperature prediction model:
T(i)＝f θ1 (i)；
calculating the predicted temperature T of each pixel point of the row according to the temperature prediction model f (i)；
S15, calculating a predicted temperature difference E (i) of each pixel point of the row:
E(i)＝T(i)-T f (i)；
calculate E (i) in the row>The number of pixel points of 0 is denoted as N pos+ Calculate E (i) in the row<The number of pixel points of 0 is denoted as N pos- ；
S16, judging whether N is satisfied pos+ -2N pos- Not less than 0; no, step S17 is performed; if yes, the pixel point i' is used as a non-defective pixel point to be recorded into the non-defective pixel point set, and then the step S17 is executed;
s17, judging whether the i' is traversed (2, 3, 4 … … and N-1); if not, updating the i ', i' to continue to take values in (2, 3, 4 … … and N-1), and returning to the step S13; if yes, go to step S18;
s18, forming sample data (i ', T (i ')) by each non-defective pixel point i ' in the non-defective pixel point set and the corresponding measured temperature T (i ')), and fitting a temperature fitting model by combining all sample data to solve model parameters theta = theta ';
s19, obtaining an uneven temperature background model of the row, and marking as:
T g (i)＝f θ' (i)；
wherein T is g (i) A non-uniform temperature background temperature value representing the ith pixel point of the row;
and calculating the non-uniform temperature background temperature value of the row of pixel points by combining the non-uniform temperature background model to form the non-uniform temperature background of the row.
Preferably, the temperature fitting model may be of parabolic formula, denoted as:
T(i)＝ai 2 +bi+c
wherein a, b and c are parabolic parameters, and θ is a set of a, b and c.
Preferably, let i' =2 in S12, S17 specifically be: judging whether i' is smaller than N-1; if yes, let i '=i' +1, then return to step S13; if not, step S18 is performed.
Preferably, the model parameters θ are solved in S18 using least squares fitting.
The thermal imaging defect detection method provided by the invention realizes the accurate removal of the uneven temperature background in the thermal imaging detection technology.
A thermal imaging defect detection method comprising the steps of:
s21, acquiring a thermal imaging image of a detected object as a detected image, and acquiring an uneven temperature background of the detected image by adopting the method for acquiring the uneven temperature background of the thermal imaging;
s22, calculating a background removing temperature value of each pixel point on the detected image, wherein the background removing temperature value is a difference value between a measured temperature value of the pixel point and a non-uniform temperature background temperature value;
s23, analyzing defect information according to the background-removing temperature value of each pixel point on the detected image.
The invention provides a thermal imaging defect detection system, which provides a carrier for the thermal imaging defect detection method.
A thermal imaging defect detection system comprising a memory having stored therein a computer program which when executed is adapted to carry out the thermal imaging defect detection method described above.
The thermal imaging defect detection system is characterized by comprising a memory and a processor, wherein a computer program is stored in the memory, the processor is connected with the memory, and the processor executes the computer program to realize the thermal imaging defect detection method.
Preferably, the device further comprises a thermal imaging module, wherein the thermal imaging module is used for acquiring thermal imaging images of the detected objects, and the processor is connected with the thermal imaging module.
The invention has the advantages that:
(1) According to the method for acquiring the non-uniform temperature background of the thermal imaging, the non-defective pixel points are selected according to the set rules, and then the non-uniform temperature background of the thermal imaging image is constructed through the non-defective pixel points, so that accurate calculation of the non-uniform temperature background of the thermal imaging image is realized, and a foundation is laid for eliminating the non-uniform temperature background.
(2) According to the invention, a parabolic formula is adopted as a temperature fitting model, so that the temperature distribution characteristic on the thermal imaging image is met, and the analysis precision of the thermal imaging image is improved.
(3) In the invention, each pixel point in a single row of pixel points is judged one by one in a sequential moving mode, which is beneficial to avoiding missing non-defective pixel points, ensuring accurate calculation of uneven temperature background and realizing efficient processing of data.
(4) In the invention, the least square method is adopted to fit the parameters of the non-uniform temperature background model, so that the method is efficient and quick and has strong applicability.
(5) According to the thermal imaging defect detection method provided by the invention, firstly, the uneven temperature background of the detection image is obtained by adopting the thermal imaging uneven temperature background obtaining method, and then the uneven temperature background is subtracted from the detection image sequence collected by the thermal camera frame by frame, so that the uneven temperature background is removed, and the aim of enhancing the infrared thermal imaging defect detection result is fulfilled. The detection method not only effectively avoids the defect of inaccurate calculated uneven temperature background caused by calculating all pixel points, but also avoids the problem that an optimization iterative algorithm is used to fall into a local optimal solution.
(6) The thermal imaging defect detection system provided by the invention provides a carrier for implementing the thermal imaging defect detection method, and is convenient for popularization of the method.
Drawings
FIG. 1 is a schematic diagram of an infrared thermal imaging detection system;
1. an infrared thermal phase meter; 2. a computer; 3. a pulse generator; 4. a light source; 100. detecting an object; 101. defects on the object are detected.
FIG. 2 is a flow chart of a method for acquiring a thermal imaging non-uniform temperature background;
FIG. 3 is a flow chart of a thermal imaging defect detection method;
FIG. 4 is a detailed view of finding non-defective location pixels according to an embodiment of the present invention;
FIG. 5 is a diagram of a pixel point at a non-defective location found by an embodiment of the present invention;
fig. 6 shows a comparison of temperature data before and after removing the heating background for a row of pixels in the detected image in the embodiment of the present invention.
Detailed Description
Example 1
According to the method for acquiring the thermal imaging uneven temperature background, uneven background temperature of each row of pixel points of a thermal imaging image is acquired first, and then the uneven background temperature of each row of pixel points is integrated into the uneven background temperature of the whole thermal imaging image according to row ordering.
In this embodiment, the method for obtaining the non-uniform temperature background of each row of pixel points includes the following steps:
s11, setting a non-defect pixel point set and a temperature fitting model: in this embodiment, the temperature fitting model uses a parabolic formula as described in formula (1.1).
T(i)＝ai 2 +bi+c (1.1)
Wherein T (i) is the measured temperature value of the ith pixel point of the row; a. b and c are parabolic parameters.
S12, let i' =2;
s13, substituting temperature data (1, T (1)), (N, T (N)), (i ', T (i ')) into the formula (1), solving a, b and c, (i ', T (i ')) represents that the measured temperature value of the ith pixel point of the row is T (i '), ((1, T (1)), (N, T (N)) is the same, and N is the number of pixels in each row of the image, namely the number of columns of the pixels.
S14, substituting a, b and c into a formula (1.1) to obtain a temperature prediction formula:
T f (i)＝ai 2 +bi+c (2.1)；
it is noted that a, b, and c are unknown quantities in formula (1.1), and a, b, and c are known quantities in formula (2.1).
According to the above formula (2.1), the predicted temperature T of each pixel point of the row is calculated f (i)；
S15, calculating a predicted temperature difference E (i) of each pixel point of the row:
E(i)＝T(i)-T f (i)；
calculate E (i) in the row>The number of pixel points of 0 is denoted as N pos+ Calculate E (i) in the row<The number of pixel points of 0 is denoted as N pos- ；
S16, judging whether N is satisfied pos+ -2N pos- Not less than 0; no, step S17 is performed; if yes, the pixel point i' is used as a non-defective pixel point to be recorded into the non-defective pixel point set, and then the step S17 is executed;
s17, if i ' < N-1, making i ' =i ' +1, and returning to the step S13; if i' =n-1, step S18 is performed.
S18, forming sample data (i ', T (i ')) by each non-defective pixel point i ' in the non-defective pixel point set and the corresponding measured temperature T (i ')), and fitting the formula (1.1) by combining all the sample data to solve model parameters a, b and c, wherein the model parameters are denoted as a=a ', b=b ', c=c ';
s19, obtaining an uneven temperature background model of the row, and marking as:
T g (i)＝a'i 2 +b'i+c'； (4.1)
wherein T is g (i') represents a non-uniform temperature background temperature value for the ith pixel point of the row;
and calculating the non-uniform temperature background temperature value of the row of pixel points by combining the non-uniform temperature background model to form the non-uniform temperature background of the row.
Specifically, in this embodiment, in final integration, the non-uniform temperature background temperature value of each pixel point may be represented by combining the row information, for example, the non-uniform temperature background temperature value of the ith pixel point of the jth row may be represented as T g (J, I) assuming that the whole thermal imaging image consists of J rows and I columns of pixel points, the non-uniform temperature background T of the thermal imaging image g Can be expressed as the following matrix:
it should be noted that, in implementation, other models, such as multiple regression equations, may be used as the temperature fitting model.
In this embodiment, after all the non-defective pixel points are obtained, a least square method may be used to fit parameters on the basis of equation (1.1) to obtain an uneven temperature background model, i.e., equation (4.1).
In this embodiment, S13-S16 is performed by i' traversing the set of numbers (2, 3, 4, … …, N-1) to obtain all non-defective pixels in the thermal imaging image. In the embodiment, the number sets (2, 3, 4 … … and N-1) are traversed in a sequential execution mode, so that the method is efficient and not prone to error; in the implementation, values can be taken from the number sets (2, 3, 4, … … and N-1) in a random mode, but each value in the number sets needs to be ensured to participate in operation.
Example 2
The embodiment provides a thermal imaging defect detection method, which specifically comprises the following steps:
s21, acquiring a thermal imaging image of a detected object as a detected image, and acquiring an uneven temperature background of the detected image by adopting the method for acquiring the uneven temperature background of the thermal imaging;
s22, calculating a background removing temperature value of each pixel point on the detected image, wherein the background removing temperature value is a difference value between a measured temperature value of the pixel point and a non-uniform temperature background temperature value;
s23, analyzing defect information according to the background-removing temperature value of each pixel point on the detected image.
In this embodiment, by subtracting the measured temperature value from the non-uniform temperature background temperature value, the non-uniform temperature background of the target image is removed, and the defect information of the detected object is resolved according to the difference value, so that the adverse effect of the non-uniform temperature background on the defect information resolution in the thermal imaging process is eliminated, the defect of the detected object obtained by final resolution is more accurate, and erroneous judgment and omission are avoided. It should be noted that, in the step S23 of the present embodiment, the method for detecting the defect information of the object according to the background-removed temperature distribution of the detected image is the prior art, and will not be described herein.
The thermal imaging defect detection method in this embodiment may specifically be divided into the following steps:
step 1: a thermal imaging model such as an infrared thermal phase meter is used for acquiring a thermal imaging image of a detected object as a detected image, a measured temperature value at an ith row and an ith column of pixels on the detected image is recorded as T (J, I), J is more than or equal to 1 and less than or equal to J, I is more than or equal to 1 and less than or equal to J, J represents the number of rows of pixels on the detected image, and I represents the number of columns of pixels on the detected image.
Step 2: a set of non-defective pixel points and a temperature fitting model as shown in formula (1.2) are set for each row:
T(j,i)＝ai 2 +bi+c (1.2)；
step 3: let i' =2;
step 4: combining the measured temperature values of the pixel points with the coordinates of the pixel points being (j, 1), (j, N) and (j, i') with the formula (1.2), the following extension formula is obtained:
according to formula (1.21), a, b, c can be solved to obtain a=a 1 ,b＝b 1 ,c＝c 1
Step 5: let a=a 1 ,b＝b 1 ,c＝c 1 Substituting the formula (1.2) to obtain a temperature prediction formula:
T f (j,i)＝a 1 i 2 +b 1 i+c 1 (2.2)；
according to the above formula (2.2), calculating the predicted temperature value of each pixel point in the j-th row, namely T f (j,i)、T f (j,i)、…T f (j,i)、…T f (j,I)。
Step 6: calculating a predicted temperature difference E (j, i) of each pixel point in the j-th row:
E(j,i)＝T(j,i)-T f (j,i)；
calculation of E (j, i) in line j>The number of pixel points of 0 is denoted as N pos+ Calculate E (j, i) in line j<The number of pixel points of 0 is denoted as N pos- ；
Step 7: whether or not to meet N pos+ -2N pos- Not less than 0; if not, executing the step 8; if yes, the pixel point (j, i') is used as a non-defective pixel point to be recorded into a non-defective pixel point set corresponding to the j-th row of the detection image, and then the step 8 is executed;
step 8: judging whether i' is smaller than N-1; if not, updating i '=i' +1, and returning to the step 4; if yes, executing the step 9;
step 9: forming sample data (i ', T (j, i') j And (3) fitting the formula (1.2) by adopting a least square method in combination with all sample data to solve the model parameters of a=a ', b=b ', c=c ', and obtaining an uneven temperature background model of the j-th row pixel point of the detected image as shown in the following formula (4.2).
T g (j,i)＝a'i 2 +b'i+c' (4.2)
Step 10: the j-th row of the detected image is carried into a non-uniform temperature background model to obtain a non-uniform temperature background temperature value, and the temperature information of the j-th row of the detected image can be expressed as { (j, k), T g (j,k)} k∈R The method comprises the steps of carrying out a first treatment on the surface of the Wherein R= {1, 2, …, I, …, I }, (j, k) represents the kth pixel point of the jth line of the detection image, T (j, k) represents the measured temperature value of the kth pixel point of the jth line of the detection image, T g (j, k) represents the non-uniform temperature of the kth pixel point of the jth row of the detection imageBackground temperature value.
Step 11: according to the above steps 2 to 10, temperature information of the image is detected, and is recorded as: { (h, k), T g (h,k)} k∈R,h∈H ，R＝{1、2、…、i、…、I},H＝{1、2、…、i、…、J}；
Wherein (h, k) represents the kth pixel point of the h row of the detection image, T (h, k) T (h, k) represents the measured temperature value of the kth pixel point of the h row of the detection image, T g (h, k) represents the non-uniform temperature background temperature value of the kth pixel point of the h row of the detection image.
Step 12: and for each pixel point of the whole thermal image, calculating a measured temperature value minus a corresponding uneven temperature background temperature value to obtain target data so as to analyze and detect defect information of the object according to the target data. Specifically, the target data may be expressed as { (h, k), T (h, k) -T g (h,k)} k∈R,h∈H ；T(h,k)-T g (h, k) represents target data corresponding to the pixel point (h, k) on the detection image.
Because the target data is temperature data after the uneven temperature background is removed by measuring the temperature, the target data is temperature data only containing defect information, can be used for analyzing the defect information of the image, and eliminates the adverse effect of the uneven background on the analysis accuracy.
In fig. 4-6, in the actual operation process of an embodiment, the pixel size definitions are different, and the number of pixels divided by one image is different, so that the abscissa in fig. 4-6 replaces the serial number of the pixel with the image width. The determination of the pixel sequence number and the image length and width position according to the pixel size is common knowledge in the art, and will not be described herein.
Fig. 4 and 5 show a process of selecting non-defective pixels according to temperature values, wherein the raw data are the pixel values and the measured temperature values. Fig. 6 shows the contrast of the gray value of the detected image after the non-uniform temperature background is removed with the gray value of the original detected image.
When the invention is embodied, the thermal imaging defect detection method can be loaded into a thermal imaging defect detection system for implementation, and can also be loaded through a memory.
The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (5)
1. The method is characterized in that each row of pixel points of a thermal imaging image is analyzed to obtain the uneven temperature background of each row of pixel points; combining the uneven temperature backgrounds of all rows of pixel points to form an uneven temperature background of the whole thermal imaging image;
the method for acquiring the uneven temperature background of each row of pixel points comprises the following steps:
s11, setting a non-defect pixel point set and a temperature fitting model, wherein the temperature fitting model is as follows:
T(i)＝f θ (i) (1)
wherein, T (i) is the measured temperature value of the ith pixel point of the row, and f represents the mapping relation between the measured temperature value T (i) and the pixel point ordering i; θ represents model parameters of the temperature fitting model;
s12, let i' be any value in a plurality (2, 3, 4 … … N-1);
s13, substituting temperature data ((1, T (1)), (N, T (N)), (i ', T (i ')) into a temperature fitting model, solving a model parameter theta=θ1, (i ', T (i ')) represents a measured temperature value of the ith pixel point of the row as T (i '),; N is the number of pixel points of each row of the thermal imaging image;
s4, substituting θ=θ1 into the temperature fitting model to obtain a temperature prediction model:
T(i)＝f θ1 (i) (2)；
calculating the predicted temperature T of each pixel point of the row according to the temperature prediction model f (i)；
S15, calculating a predicted temperature difference E (i) of each pixel point of the row:
E(i)＝T(i)-T f (i)； (3)
calculate E (i) in the row>The number of pixel points of 0 is denoted as N pos+ Calculate E (i) in the row<The number of pixel points of 0 is denoted as N pos- ；
S16, judging whether N is satisfied pos+ -2N pos- Not less than 0; no, step S17 is performed; if yes, the pixel point i' is used as a non-defective pixel point to be recorded into the non-defective pixel point set, and then the step S17 is executed;
s17, judging whether the i' is traversed (2, 3, 4 … … and N-1); if not, updating the i ', i' to continue to take values in (2, 3, 4 … … and N-1), and returning to the step S13; if yes, go to step S18;
s18, forming sample data (i ', T (i ')) by each non-defective pixel point i ' in the non-defective pixel point set and the corresponding measured temperature T (i ')), and fitting a temperature fitting model by combining all sample data to solve model parameters theta = theta ';
s19, obtaining an uneven temperature background model of the row, and marking as:
T g (i)＝f θ' (i)； (4)
wherein T is g (i') represents a non-uniform temperature background temperature value for the ith pixel point of the row;
and calculating the non-uniform temperature background temperature value of the row of pixel points by combining the non-uniform temperature background model to form the non-uniform temperature background of the row.
2. The method of claim 1, wherein the temperature fitting model is a parabolic equation, denoted as:
T(i)＝ai 2 +bi+c (1.1)
wherein a, b and c are parabolic parameters, and θ is a set of a, b and c.
3. The method for obtaining a thermal imaging non-uniform temperature background according to claim 1, wherein in S12, let i' =2, S17 specifically be: judging whether i' is smaller than N-1; if yes, let i '=i' +1, then return to step S13; if not, step S18 is performed.
4. The method of claim 1, wherein the model parameters θ are solved in S18 using least squares fitting.
5. A method for detecting a thermal imaging defect, comprising the steps of:
s21, acquiring a thermal imaging image of a detected object as a detected image, and acquiring an uneven temperature background of the detected image by adopting the thermal imaging uneven temperature background acquisition method according to any one of claims 1-4;
s22, calculating a background removing temperature value of each pixel point on the detected image, wherein the background removing temperature value is a difference value between a measured temperature value of the pixel point and a non-uniform temperature background temperature value;
s23, analyzing defect information according to the background-removing temperature value of each pixel point on the detected image.
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