Eddy current thermal imaging defect detection method based on temperature gradient processing

Abstract

The invention discloses an eddy current thermal imaging defect detection method based on temperature gradient processing, and relates to the technical field of eddy current thermal imaging defect detection, and the method comprises the steps: calibrating a camera in a thermal imager camera system, and obtaining a corresponding relation between a spatial position and a pixel position; carrying out eddy current heating on the metal surface to be detected, and shooting a continuous thermal imaging image of the metal in a cooling stage after heating by using a thermal imager; arranging the thermal imaging images in the cooling stage according to a time sequence; selecting the spatial position corresponding to the pixel point with the highest temperature in the first frame image as the central position of the metal surface; estimating the partial derivative of the temperature T to x at the central position of the metal surface, and taking the estimated value as the temperature gradient at the central position of the metal surface; and drawing a temperature gradient-time curve graph according to the temperature gradient at the central position of the metal surface at each moment, and comparing the temperature gradient-time curve graph with a theoretical curve graph to determine the type of the defect. The thermal imaging image can be processed by using the temperature gradient, so that the metal surface defect and the subsurface defect can be distinguished.

Description

Technical Field

[0001] The invention relates to the technical field of eddy current thermal imaging defect detection, in particular to an eddy current thermal imaging defect detection method based on temperature gradient processing. Background Art

[0002] Eddy current thermal imaging defect detection technology is one of the hot topics in non-destructive testing technology research because of its advantages of non-contact and real-time, high sensitivity and high resolution, fast and efficient, reduced material damage, quantitative analysis, and wide application range. Distinguishing between surface defects and sub-surface defects is extremely important for industrial defect detection such as metal plate defect detection and welding process optimization, but there is little research on distinguishing between surface defects and sub-surface defects, especially defects of smaller size. Summary of the invention

[0003] In order to overcome the defects in the above-mentioned prior art, the present invention provides an eddy current thermal imaging defect detection method based on temperature gradient processing, which can utilize temperature gradient to process thermal imaging images, thereby achieving the distinction between metal surface defects and sub-surface defects.

[0004] To achieve the above object, the present invention adopts the following technical solutions, including:

[0005] The eddy current thermal imaging defect detection method based on temperature gradient processing includes the following steps:

[0006] Step 1, calibrating the camera intrinsic parameters in the thermal imager camera system;

[0007] Step 2, determining the camera extrinsic parameters in the thermal imager camera system with the surface of the object as the plane z=0, and obtaining the corresponding relationship between the spatial position and the pixel position;

[0008] Step 3, eddy current heating the metal surface to be tested, and using a thermal imager to capture continuous thermal imaging images of the metal during the cooling stage after heating;

[0009] Step 4, arrange the thermal imaging images of the cooling stage in chronological order; select the pixel point (u, v) with the highest temperature in the first frame image, and use the spatial position (x, y) corresponding to the pixel point (u, v) with the highest temperature as the center position of the metal surface;

[0010] Step 5, estimate the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x, and use the estimated value as the temperature gradient at the center position (x, y) of the metal surface;

[0011] Step 6: Draw a temperature gradient-time curve according to the temperature gradient at the center position (x, y) of the metal surface at each time, and compare it with the theoretical temperature gradient-time curve to determine the type of defect.

[0012] Preferably, in step 5, according to the correspondence between the spatial position and the pixel position, and according to the temperature data around the center position (x, y) of the metal surface, the partial derivatives of the temperature T at the center position (x, y) of the metal surface in the four directions of (x+Δx, y), (x-Δx, y), (x, y+Δy), and (x, y-Δy) are calculated by using the differential derivation method, and the estimated value of the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x is obtained by averaging:

[0013]

[0014] In the formula, t i The partial derivatives of the temperature T at the moment in the four directions of (x+Δx,y), (x-Δx,y), (x,y+Δy), and (x,y-Δy); T i (x,y) is t i The temperature data at the center position (x, y) of the metal surface at the moment, that is, the temperature of the pixel corresponding to the spatial position (x, y); T i (x+Δx,y) is t i Temperature data of the metal surface at time (x+Δx,y); T i (x-Δx,y) is t i Temperature data of the metal surface at time (x-Δx,y); T i (x,y+Δy) is t i Temperature data of the metal surface at time (x,y+Δy); T i (x,y-Δy) is t i Temperature data of the metal surface at the moment (x, y-Δy); Δx = Δy is the actual size of the physical pixel size Δu of the image mapped on the metal surface; t i The estimated value of the partial derivative of the temperature T with respect to x at the moment is the temperature gradient at the center position (x, y) of the metal surface.

[0015] Preferably, the calculation formula of the temperature gradient at the center position (x, y) of the metal surface is converted into a convolution form:

[0016]

[0017] Where g(x,y) is the temperature gradient at the center position (x,y) of the metal surface obtained by convolution, T(x-1+k,y-1+k) represents the temperature data at the metal surface (x-1+k,y-1+k), h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, k,l=0,1,2;

[0018] The convolution kernel h(k,l) is:

[0019]

[0020] Preferably, in step 6, the specific method of determining the defect type is as follows:

[0021] If the trend of the temperature gradient-time graph is monotonically decreasing, there are no defects in the metal;

[0022] If the temperature gradient-time curve first increases rapidly to a certain value, then decreases rapidly and then becomes a slow decrease, there is a convex defect on the metal surface;

[0023] If the temperature gradient-time curve shows a sharp decrease, then increases rapidly to a certain value, and then decreases slowly, there is a concave defect on the metal surface;

[0024] If the trend of the temperature gradient-time curve is rapidly decreasing, slowly increasing, and then slowly decreasing, there are defects in the sub-surface of the metal;

[0025] Among them, the magnitude relationship of the monotonically decreasing rate Vd1, the rapidly decreasing rate Vd2, the slowly decreasing rate Vd3 and the sharply decreasing rate Vd4 is: |Vd4|>|Vd1|>|Vd2|>|Vd3|>0;

[0026] The relationship between the rapidly increasing rate Vu1 and the slowly increasing rate Vu2 is: |Vu1|>|Vu2|>0.

[0027] The present invention also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned eddy current thermal imaging defect detection method based on temperature gradient processing when executing the computer program.

[0028] The present invention also provides a computer program product, which includes a computer program / instruction, and when the computer program / instruction is executed by a processor, the above-mentioned eddy current thermal imaging defect detection method based on temperature gradient processing is implemented.

[0029] The present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed, the above-mentioned eddy current thermal imaging defect detection method based on temperature gradient processing is implemented.

[0030] The advantages of the present invention are:

[0031] (1) The present invention draws a temperature gradient-time curve and compares it with the trend of the theoretical temperature gradient-time curve to distinguish metal surface defects and sub-surface defects, which can reduce the influence of various factors. It is particularly useful for small defects that are sensitive to the surrounding environment, and is generally applicable to defects of various sizes and shapes.

[0032] (2) The present invention utilizes the correspondence between pixel position and spatial position and the differential method to estimate the partial derivative of temperature T with respect to x at the center position (x, y) of the metal surface. Since metal is anisotropic, the partial derivative of temperature T with respect to x can be used as the temperature gradient at the center position (x, y) of the metal surface.

[0033] (3) In order to reduce the influence of noise when taking images and reduce errors, the present invention proposes calculating the partial derivatives of the temperature T at the center position (x, y) of the metal surface in the four directions of (x+Δx, y), (x-Δx, y), (x, y+Δy), and (x, y-Δy), and using their average value as the estimated value of the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x, thereby eliminating the influence of noise.

[0034] (4) In order to facilitate calculation, the present invention also provides a convolution formula for calculating the temperature gradient at the center position (x, y) of the metal surface.

[0035] (5) The electronic device, computer program product and readable storage medium disclosed in the present invention can produce the same beneficial effects as the above method by applying the above method, which will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Flow chart of the method of the present invention.

[0037] Figure 2 Schematic diagram of the convolution kernel h(k,l).

[0038] Figure 3 Schematic diagram of convolution of the method of the present invention.

[0039] Figure 4 Temperature gradient-time curves for various types of defects. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] This embodiment provides a method for detecting metal surface defects and sub-surface defects by processing eddy current thermal imaging images using temperature gradient. During the eddy current thermal imaging image processing, the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x is calculated by the difference method and the average method and replaced with the temperature gradient size. Then, a line graph of the temperature gradient size-time is drawn and compared with the theoretical graph to determine the defect type. Figure 1 As shown, the method is performed in the following steps:

[0043] Step 1: In the thermal imager system, the camera intrinsic parameter A is calibrated using the Zhang Zhengyou calibration method and a specific calibration plate according to the pinhole camera model. c .

[0044] Step 2: Taking the surface of the object as the plane with z=0, determine the external parameters of the thermal imager camera system, that is, the corresponding relationship between the spatial position and the pixel position.

[0045] Step 3: eddy current heat the metal surface with defects, and use a thermal imager to take continuous thermal imaging images of the metal during the heating and cooling stages.

[0046] Step 4: Filter out the thermal imaging images of the cooling stage from the captured images, and arrange the thermal imaging images of the cooling stage in chronological order and label them (t0, T0), (t1, T1), (t2, T2), ..., (t n ,T n ), where t i Indicates time; T i Indicates t i Temperature data of the image at time T i Including the temperature data of each pixel in the image; n≥59; select the pixel (u, v) with the highest temperature in the first frame image (t0, T0), and use the spatial position (x, y) corresponding to the pixel (u, v) with the highest temperature as the center position of the metal surface.

[0047] Step 5: Estimate the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x, and reduce the influence of noise to reduce the error.

[0048] Step 5.1, according to the corresponding relationship between the spatial position (x, y) and the pixel position (u, v), the partial derivative calculation formula of temperature T with respect to x is obtained by using the differential derivation method:

[0049]

[0050] In the formula, t i The partial derivative of the temperature T with respect to x at the moment Ti (x+Δx,y) is t i The temperature data of the metal surface at the moment (x+Δx, y), that is, the temperature of the pixel corresponding to the spatial position (x+Δx, y); T i (x,y) is t i The temperature data at the metal surface at the moment (x, y), that is, the temperature of the pixel corresponding to the spatial position (x, y); Δx is the actual size of the pixel physical size Δu of the image mapped on the metal surface.

[0051] Step 5.2: Using the temperature data around the center position (x, y) of the metal surface, calculate the partial derivatives of the temperature T at the center position (x, y) of the metal surface in the four directions of (x+Δx, y), (x-Δx, y), (x, y+Δy), and (x, y-Δy), and use their average value as the estimated value of the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x, thereby eliminating the influence of noise.

[0052]

[0053] Because the metal is anisotropic and Δx = Δy, the estimated value of the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x is:

[0054]

[0055] The result of the above formula (3) is used to replace the temperature gradient at the center position (x, y) of the metal surface.

[0056] Depend on Figure 2 and Figure 3 As shown, for the convenience of calculation, the present invention can calculate the temperature gradient at the center position (x, y) of the metal surface in the form of convolution:

[0057]

[0058] Among them, g(x,y) is the temperature gradient at the center position (x,y) of the metal surface obtained by convolution; T(x-1+k,y-1+k) represents the temperature data at the metal surface (x-1+k,y-1+k), h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, k,l=0,1,2;

[0059] The convolution kernel h(k,l) is:

[0060]

[0061] Step 6: According to formula (3), the temperature gradient at the center position (x, y) of the metal surface at each time is obtained: At least n sets of data, plotting temperature gradient-time The curve diagram is compared with the theoretical temperature gradient-time curve diagram to determine the type of defect. The theoretical temperature gradient-time curve diagram includes temperature gradient-time curve diagrams of various defects and no defects.

[0062] If the trend of the temperature gradient-time curve is monotonically decreasing, there are no defects in the metal.

[0063] If the temperature gradient-time curve first increases rapidly to a certain size, then decreases rapidly and then becomes a slow decrease, there is a convex defect on the metal surface.

[0064] If the temperature gradient-time curve decreases sharply, then increases rapidly to a certain size, and then decreases slowly, there is a concave defect on the metal surface.

[0065] If the temperature gradient-time curve shows a rapid decrease, a slow increase, and then a slow decrease, there are defects on the sub-surface of the metal.

[0066] Among them, the relationship between the monotonically decreasing rate Vd1, the rapidly decreasing rate Vd2, the slowly decreasing rate Vd3 and the sharply decreasing rate Vd4 is: |Vd4|>|Vd1|>|Vd2|>|Vd3|>0.

[0067] The relationship between the rapidly increasing rate Vu1 and the slowly increasing rate Vu2 is: |Vu1|>|Vu2|>0.

[0068] Depend on Figure 4 The figure shows the temperature gradient-time curves of various types of defects. Among them, (a) is the temperature gradient-time curve of no defect, (b) is the temperature gradient-time curve of surface convex defect, (c) is the temperature gradient-time curve of surface concave defect, and (d) is the temperature gradient-time curve of sub-surface defect.

[0069] Example 2

[0070] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the eddy current thermal imaging defect detection method based on temperature gradient processing of the above-mentioned embodiment 1 when executing the computer program.

[0071] The electronic device of the embodiment of the present application can be the mobile device itself, or a stand-alone device independent of the mobile device, which can communicate with the mobile device to receive the collected input signals from them and send the selected target decision behavior to them.

[0072] The electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement the decision behavior decision method and / or other desired functions of the various embodiments of the present application described above.

[0073] Electronic devices may also include input devices and output devices.

[0074] Example 3

[0075] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the decision-making behavior decision method according to various embodiments of the present application described in the above-mentioned embodiment 1 of this specification.

[0076] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0077] Example 4

[0078] An embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the decision-making behavior decision method according to various embodiments of the present application described in the above-mentioned embodiment 1.

[0079] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0080] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0081] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Eddy current thermal imaging defect detection method based on temperature gradient processing, characterized in that: The following steps are involved: Step 1, calibrating the camera intrinsic parameters in the thermal imager camera system; Step 2, determining the camera extrinsic parameters in the thermal imager camera system with the surface of the object as the plane z=0, and obtaining the corresponding relationship between the spatial position and the pixel position; Step 3, eddy current heating the metal surface to be tested, and using a thermal imager to capture continuous thermal imaging images of the metal during the cooling stage after heating; Step 4, arrange the thermal imaging images of the cooling stage in chronological order; select the pixel point (u, v) with the highest temperature in the first frame image, and use the spatial position (x, y) corresponding to the pixel point (u, v) with the highest temperature as the center position of the metal surface; Step 5, estimate the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x, and use the estimated value as the temperature gradient at the center position (x, y) of the metal surface; Step 6: Draw a temperature gradient-time curve according to the temperature gradient at the center position (x, y) of the metal surface at each time, and compare it with the theoretical temperature gradient-time curve to determine the type of defect.

2. The eddy current thermal imaging defect detection method based on temperature gradient processing according to claim 1 is characterized in that: In step 5, according to the correspondence between the spatial position and the pixel position, and according to the temperature data around the center position (x, y) of the metal surface, the differential derivation method is used to calculate the partial derivatives of the temperature T at the center position (x, y) of the metal surface in the four directions of (x+Δx, y), (x-Δx, y), (x, y+Δy), and (x, y-Δy), and the average is used to obtain the estimated value of the partial derivative of the temperature T at the center position (x, y) of the metal surface with respect to x: In the formula, t i The partial derivatives of the temperature T at the moment in the four directions of (x+Δx,y), (x-Δx,y), (x,y+Δy), and (x,y-Δy); T i (x,y) is t i The temperature data at the center position (x, y) of the metal surface at the moment, that is, the temperature of the pixel corresponding to the spatial position (x, y); T i (x+Δx,y) is t i Temperature data of the metal surface at time (x+Δx,y); T i (x-Δx,y) is t i Temperature data of the metal surface at time (x-Δx,y); T i (x,y+Δy) is t i Temperature data of the metal surface at time (x,y+Δy); T i (x,y-Δy) is t i Temperature data of the metal surface at the moment (x, y-Δy); Δx = Δy is the actual size of the physical pixel size Δu of the image mapped on the metal surface; t i The estimated value of the partial derivative of the temperature T with respect to x at the moment is the temperature gradient at the center position (x, y) of the metal surface.

3. The eddy current thermal imaging defect detection method based on temperature gradient processing according to claim 2 is characterized in that: Convert the calculation formula of the temperature gradient at the center position (x, y) of the metal surface into convolution form: Where g(x,y) is the temperature gradient at the center position (x,y) of the metal surface obtained by convolution, T(x-1+k,y-1+k) represents the temperature data at the metal surface (x-1+k,y-1+k), h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, k,l=0,1,2; The convolution kernel h(k,l) is:

4. The eddy current thermal imaging defect detection method based on temperature gradient processing according to claim 1 is characterized in that: In step 6, the specific method for determining the defect category is as follows: If the trend of the temperature gradient-time graph is monotonically decreasing, there are no defects in the metal; If the temperature gradient-time curve first increases rapidly to a certain value, then decreases rapidly and then becomes a slow decrease, there is a convex defect on the metal surface; If the temperature gradient-time curve shows a sharp decrease, then increases rapidly to a certain value, and then decreases slowly, there is a concave defect on the metal surface; If the trend of the temperature gradient-time curve is rapidly decreasing, then slowly increasing, and then slowly decreasing, there are defects in the subsurface of the metal; Among them, the magnitude relationship of the monotonically decreasing rate Vd1, the rapidly decreasing rate Vd2, the slowly decreasing rate Vd3 and the sharply decreasing rate Vd4 is: |Vd4|>|Vd1|>|Vd2|>|Vd3|>0; The relationship between the rapidly increasing rate Vu1 and the slowly increasing rate Vu2 is: |Vu1|>|Vu2|>0.

5. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the eddy current thermal imaging defect detection method based on temperature gradient processing according to any one of claims 1 to 4 is implemented.

6. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the eddy current thermal imaging defect detection method based on temperature gradient processing as described in any one of claims 1-4.

7. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the eddy current thermal imaging defect detection method based on temperature gradient processing as described in any one of claims 1 to 4 is implemented.

Images