A material damage defect edge extraction method based on infrared thermal image sequence

Abstract

The invention discloses a material damage defect edge extraction method based on infrared thermal image sequence, which solves the problem of adaptive extraction of material damage defect edges in infrared thermal image sequence. The key points of the technical scheme include: firstly, obtaining an infrared thermal image sequence of the original material cooling process, then calculating the attenuation rate according to the temperature sequence corresponding to each pixel point, obtaining a specimen attenuation rate characteristic diagram, then constructing a fitting characteristic curve function corresponding to each row and each column by row and column for the attenuation rate characteristic values ​​corresponding to the elements in the characteristic diagram, calculating a derivative of the characteristic curve function once, obtaining a specimen attenuation acceleration diagram, and finally searching for extreme value points on the specimen attenuation acceleration diagram, connecting the obtained extreme value points to obtain the damage defect edge, and realizing material damage defect edge extraction.

Description

Technical Field

[0001] The invention belongs to the technical field of image processing and infrared nondestructive testing, and in particular relates to a material damage defect edge extraction method based on infrared thermal image sequence. Background Art

[0002] Infrared nondestructive testing technology is a nondestructive testing method that uses the infrared radiation characteristics of the object to be tested to identify damage and has broad application prospects. Infrared nondestructive testing is divided into active and passive types according to whether there is an external excitation source. Passive infrared thermal imaging nondestructive testing is mainly used for self-heating electronic components or equipment in working state. Active infrared thermal imaging nondestructive testing changes the thermal equilibrium state of the object through an external excitation source, and inverts its internal or surface damage information based on the temperature characteristics of the object to be tested. Therefore, it has a wider range of applications. The defect feature extraction method and image processing method for this detection method have also achieved many research results.

[0003] The process of active infrared thermal imaging nondestructive testing can be divided into a heating stage and a cooling stage. Generally, the temperature change in the cooling stage is more stable than that in the heating stage, and the thermal image sequence data in the cooling stage is often collected for analysis. Infrared thermal image sequence processing is divided into single-frame image processing and multi-frame image processing. Multi-frame images reflect the process of temperature change during the entire sampling period and contain more comprehensive information. Commonly used multi-frame image feature extraction methods include: singular value decomposition, thermal image signal reconstruction, and principal component analysis. These methods integrate the information of the image sequence in time and space, separate defect feature information and background noise information, and achieve defect feature enhancement. However, the existing methods all require the establishment of a large number of sample libraries for algorithm training, and cannot adaptively extract damage defect edges from the currently collected thermal image sequence. Their real-time performance cannot meet the needs of online damage identification, and there are great limitations for practical applications. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a material damage defect edge extraction method based on infrared thermal image sequence, which solves the problem that the existing algorithm requires sample accumulation and has poor real-time performance.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A material damage defect edge extraction method based on infrared thermal image sequence comprises the following steps:

[0007] S1: Pulse heating is performed on the surface of the defect-free side of the test piece, and a sequence of infrared thermal images of the defect-free side surface in the cooling stage is collected;

[0008] S2: extract the corresponding temperature value of the acquired infrared thermal image sequence by pixel, obtain the temperature sequence corresponding to each pixel point in the cooling process, calculate the decay rate corresponding to each pixel point, and obtain the decay rate characteristic diagram of the specimen cooling process;

[0009] S3: Fitting the decay rate characteristic value curve function corresponding to each row and column of the decay rate characteristic diagram by row and column, calculating the first derivative of the characteristic curve function, and obtaining the specimen decay acceleration diagram;

[0010] S4: Search for extreme points on the attenuation acceleration diagram by row and column respectively, and connect the obtained extreme points to obtain the damage defect edge.

[0011] Its further technical solution is:

[0012] Step S2, extracting the corresponding temperature value of the collected infrared thermal image sequence by pixel, obtaining the temperature sequence corresponding to each pixel point in the cooling process, calculating the decay rate corresponding to each pixel point, and obtaining the decay rate characteristic diagram of the specimen cooling process, including the following steps:

[0013] The infrared thermal image sequence consists of N frames of infrared thermal images collected. Each frame of infrared thermal image after grayscale conversion is regarded as the matrix A n , n is the frame number, n = 1, 2, ..., N, then the gray value of each pixel in the matrix is ​​regarded as the current measured temperature value A n (x, y), where x (1≤x≤X) is the matrix row number, X is the number of matrix rows, y (1≤y≤Y) is the matrix column number, and Y is the number of matrix columns;

[0014] The temperature values ​​corresponding to the pixels (x, y) of each frame in the infrared thermal image sequence are extracted respectively to obtain the temperature sequence T at the pixel (x, y) during the cooling process. xy (t), use m-order polynomial to fit the expression of logarithmic domain:

[0015] ln[T xy (t)] = c0 + c1ln(t) + c2[ln(t)] 2 +…+c i [ln(t)] i +…+c m [ln(t)] m

[0016] Among them, ln() is the logarithm with constant e as base, c0, c1, c i 、c m are the polynomial coefficients;

[0017] Set the start time t1 and the end time t2 respectively, t2>t1, and calculate the temperature difference experienced in the cooling process according to the fitted polynomial curve, which is regarded as the temperature decay rate vxy ;

[0018] Repeat the above steps to obtain the temperature decay rate corresponding to each pixel in the infrared thermal image, and obtain the temperature decay rate characteristic matrix V of the specimen cooling process. X×Y .

[0019] Step S3 includes the following steps:

[0020] In the characteristic matrix of temperature decay rate during cooling process, extract each row element to obtain the sequence V x ,1≤x≤X, the x-th row decay rate characteristic value curve function VX is obtained by polynomial fitting x ,1≤x≤X, for the characteristic curve function VX x Find the first derivative VX x ', and take the absolute value of the derivative result to obtain the temperature decay acceleration value curve of the cooling process;

[0021] In the characteristic matrix of temperature decay rate during cooling process, extract each column element to obtain the sequence V y ,1≤y≤Y, the y-th row decay rate characteristic value curve function VY is obtained by polynomial fitting y ,1≤y≤Y, for the characteristic curve function VY y Find the first derivative VY y ', and take the absolute value of the derivative result to obtain the temperature decay acceleration curve of the cooling process by column;

[0022] Step S4 includes the following steps:

[0023] Search the maximum value point of the obtained temperature decay acceleration value curve corresponding to each row of the cooling process;

[0024] Search the maximum value point of the obtained temperature decay acceleration value curve corresponding to the cooling process in each column;

[0025] By connecting the above-mentioned maximum points, the edge of the material damage defect can be determined.

[0026] The beneficial effects of the present invention are as follows:

[0027] The present invention collects infrared thermal image sequences of the original material cooling process, calculates the attenuation rate of the temperature sequence corresponding to each pixel point, obtains the specimen attenuation rate characteristic diagram, obtains the row and column characteristic curve functions through polynomial fitting, and obtains the first derivative thereof to obtain the specimen attenuation acceleration diagram, and finally searches for extreme value points on the specimen attenuation acceleration diagram, connects the obtained extreme value points to obtain the damage defect edge, and realizes the material damage defect edge extraction. The method can directly analyze and process the collected thermal image sequence, and adaptively extract material damage defects. Compared with the prior art, it solves the problem that the algorithm requires sample accumulation and has weak real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the disclosed method;

[0029] Figure 2 The image of the tested object in the embodiment;

[0030] Figure 3 It is the temperature decay curve and logarithmic fitting curve;

[0031] Figure 4 It is the characteristic diagram of the attenuation rate of the specimen during cooling process;

[0032] Figure 5 Fitting curve for column-wise decay rate;

[0033] Figure 6 is the temperature decay acceleration curve;

[0034] Figure 7 Display diagram for thermal image extreme points; DETAILED DESCRIPTION

[0035] The solution of this application is further described below through specific implementation:

[0036] refer to Figure 1 The present invention provides a material damage defect edge extraction method based on infrared thermal image sequence, comprising the following steps:

[0037] S1: Pulse heating is performed on the surface of the defect-free side of the test piece, and a sequence of infrared thermal images of the defect-free side surface in the cooling stage is collected;

[0038] S2: extract the corresponding temperature value of the acquired infrared thermal image sequence by pixel, obtain the temperature sequence corresponding to each pixel point in the cooling process, calculate the decay rate corresponding to each pixel point, and obtain the decay rate characteristic diagram of the specimen cooling process;

[0039] The infrared thermal image sequence consists of N = 490 frames of infrared thermal images collected. Each frame of infrared thermal image after grayscale conversion is regarded as the matrix A n , n is the frame number, n = 1, 2, ..., N, then the gray value of each pixel in the matrix is ​​regarded as the current measured temperature value A n (x, y), where x (1≤x≤X) is the matrix row number, X is the number of matrix rows, y (1≤y≤Y) is the matrix column number, Y is the number of matrix columns. Figure 2 As shown, the infrared thermal image corresponds to X=160 and Y=220;

[0040] The temperature values ​​corresponding to the pixels (x, y) of each frame in the infrared thermal image sequence are extracted respectively to obtain the temperature sequence T at the pixel (x, y) during the cooling process. xy(t), use m-order polynomial to fit the expression of logarithmic domain:

[0041] ln[T xy (t)] = c0 + c1ln(t) + c2[ln(t)] 2 +…+c i [ln(t)] i +…+c m [ln(t)] m

[0042] Among them, ln() is the logarithm with constant e as base, c0, c1, c i 、c m are polynomial coefficients; take m = 5, the fitted polynomial curve is as follows Figure 3 As shown;

[0043] Set the start time t1 to 2.8 seconds and the end time t2 to 3.2 seconds. Calculate the temperature difference experienced during the cooling process based on the fitted polynomial curve, which is regarded as the temperature decay rate v xy ;

[0044] Calculate the temperature decay rate corresponding to each pixel in the infrared thermal image and obtain the temperature decay rate characteristic matrix V of the specimen cooling process X×Y ,like Figure 4 As shown;

[0045] The decay rate characteristic value curve function corresponding to each row and column of the decay rate characteristic graph is fitted by row and column respectively, and a 20th-order polynomial model and reverse fitting are adopted. The polynomial model is as follows:

[0046] T(t)=a0+a1t+a2t 2 +…+a 20 t 20

[0047] The temperature decay rate characteristic curve of column 32 is as follows Figure 5 As shown, the characteristic curve function is derived once to obtain the temperature attenuation acceleration diagram of the specimen, as shown in Figure 6 As shown;

[0048] Search for extreme points on the attenuation acceleration graph by row and column respectively, and connect the extreme points to get the damage defect edge, such as Figure 7 As shown in the figure, the damage defect edge can be obtained and the degree of damage health can be judged.

[0049] The above are exemplary embodiments of the present disclosure, and the protection scope of the present disclosure is defined by the claims and their equivalents.

Claims

1. A material damage defect edge extraction method based on infrared thermal image sequence, characterized in that: The following steps are involved: S1: Pulse heating is performed on the surface of the defect-free side of the test piece, and a sequence of infrared thermal images of the surface of the defect-free side in the cooling stage is collected; S2: extract the corresponding temperature value of the acquired infrared thermal image sequence by pixel, obtain the temperature sequence corresponding to each pixel point in the cooling process, calculate the decay rate corresponding to each pixel point, and obtain the decay rate characteristic diagram of the specimen cooling process; S3: Fitting the decay rate characteristic value curve function corresponding to each row and column of the decay rate characteristic diagram by row and column, calculating the first derivative of the characteristic curve function, and obtaining the specimen decay acceleration diagram; S4: Search for extreme points on the attenuation acceleration diagram by row and column respectively, and connect the obtained extreme points to obtain the damage defect edge.

2. The material damage defect edge extraction method based on infrared thermal image sequence according to claim 1, characterized in that: Step S2 specifically includes: S21: The infrared thermal image sequence consists of N frames of infrared thermal images collected. Each frame of infrared thermal image after grayscale conversion is regarded as matrix A. n , n is the frame number, n = 1, 2, ..., N, then the gray value of each pixel in the matrix is ​​regarded as the current measured temperature value A n (x,y); Where x (1≤x≤X) is the matrix row number, X is the number of matrix rows, y (1≤y≤Y) is the matrix column number, Y is the number of matrix columns; S22: Extract the temperature values ​​corresponding to the pixels (x, y) of each frame in the infrared thermal image sequence respectively, and obtain the temperature sequence T at the pixel (x, y) during the cooling process. xy (t), use m-order polynomial to fit the expression of logarithmic domain: ln[T xy (t)]=c0+c1ln(t)+c2[ln(t)] 2 +…+c i [ln(t)] i +…+c m [ln(t)] m Among them, ln() is the logarithm with constant e as base, c0, c1, c i 、c m are the polynomial coefficients; S23: Set the start time t1 and the end time t2 respectively, t2>t1, and calculate the temperature difference experienced during the cooling process according to the fitted polynomial curve, which is regarded as the temperature decay rate v xy ; S24: Repeat the above steps S22-S23 to obtain the temperature decay rate corresponding to each pixel in the infrared thermal image, and obtain the temperature decay rate characteristic matrix V of the specimen cooling process. X×Y .

3. The material damage defect edge extraction method based on infrared thermal image sequence according to claim 1, characterized in that: Step S3 specifically includes: S31: In the cooling process temperature decay rate characteristic matrix, extract each row element to obtain the sequence V x ,1≤x≤X, the x-th row decay rate characteristic value curve function VX is obtained by polynomial fitting x ,1≤x≤X, for the characteristic curve function VX x Find the first derivative VX x ', and take the absolute value of the derivative result to obtain the temperature decay acceleration value curve of the cooling process; S32: In the cooling process temperature decay rate characteristic matrix, extract each column element to obtain the sequence V y ,1≤y≤Y, the y-th row decay rate characteristic value curve function VY is obtained by polynomial fitting y ,1≤y≤Y, for the characteristic curve function VY y Find the first derivative VY y ', and take the absolute value of the derivative result to obtain the temperature decay acceleration curve of the cooling process by column.

4. The material damage defect edge extraction method based on infrared thermal image sequence according to claim 1, characterized in that: Step S4 specifically includes: S41: searching for the maximum value point of the obtained temperature decay acceleration value curve corresponding to each row of the cooling process; S42: searching for the maximum value point of the obtained temperature decay acceleration value curve corresponding to each column of the cooling process; S43: By connecting the above-mentioned maximum value points, the edge of the material damage defect can be determined.

Images