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

Abstract

This invention discloses an eddy current thermal imaging defect detection method based on temperature gradient processing, belonging to the field of eddy current thermal imaging defect detection technology. The method involves calibrating the camera in a thermal imager system to obtain the correspondence between spatial and pixel positions; subjecting the metal surface to be inspected to eddy current heating and capturing continuous thermal images of the cooling phase using a thermal imager; arranging the thermal images of the cooling phase in chronological order; selecting the spatial position corresponding to the pixel with the highest temperature in the first frame as the center position of the metal surface; estimating the partial derivative of the temperature T at the center position of the metal surface with respect to x, and using the estimated value as the temperature gradient at the center position of the metal surface; plotting a temperature gradient-time curve based on the temperature gradient at the center position of the metal surface at various times, and comparing it with a theoretical curve to determine the type of defect. This invention can utilize temperature gradient processing of thermal imaging images to distinguish between surface defects and subsurface defects in metals.

Description

Technical Field

[0001] This invention relates to the field of eddy current thermal imaging defect detection technology, and in particular to an eddy current thermal imaging defect detection method based on temperature gradient processing. Background Technology

[0002] Eddy current thermal imaging defect detection technology is currently a hot topic in nondestructive testing (NDT) research due to its advantages such as non-contact and real-time operation, high sensitivity and high resolution, rapid and efficient operation, reduced material damage, quantitative analysis, and wide applicability. Distinguishing between surface and subsurface defects is extremely important for industrial defect detection, such as metal plate defect detection and welding process optimization. However, current research on distinguishing between surface and subsurface defects, especially small-sized defects, is scarce. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, the present invention provides an eddy current thermal imaging defect detection method based on temperature gradient processing, which can utilize temperature gradient processing of thermal imaging images to distinguish between metal surface defects and subsurface defects.

[0004] To achieve the above objectives, the present invention adopts the following technical solution, including:

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

[0006] Step 1: Calibrate the camera intrinsic parameters in the thermal imaging camera system;

[0007] Step 2: Determine the camera extrinsic parameters in the thermal imager camera system using the object surface as the plane with z=0, and obtain the correspondence between spatial position and pixel position;

[0008] Step 3: Perform eddy current heating on the metal surface to be tested, and use a thermal imager to capture continuous thermal 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 (u,v) with the highest temperature in the first frame image, 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;

[0010] Step 5: Estimate the partial derivative of 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: Based on the temperature gradient at the center position (x, y) of the metal surface at each time point, plot the temperature gradient-time curve and compare it with the theoretical temperature gradient-time curve to determine the type of defect.

[0012] Preferably, in step 5, based on the correspondence between spatial location and pixel location, and based on the temperature data surrounding the center location (x,y) of the metal surface, the partial derivatives of the temperature T at the center location (x,y) of the metal surface with respect to the four directions (x+Δx,y), (x-Δx,y), (x,y+Δy), and (x,y-Δy) are calculated using the differential differentiation method, and the average is used to obtain an estimated value of the partial derivative of the temperature T at the center location (x,y) of the metal surface with respect to x.

[0013]

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

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

[0016]

[0017] In the formula, 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 (x-1+k,y-1+k) of the metal surface, h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, and k,l=0,1,2;

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

[0019]

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

[0021] If the temperature gradient-time curve shows a monotonically decreasing trend, then the metal has no defects.

[0022] If the temperature gradient-time curve first increases rapidly to a certain size, then decreases rapidly and then decreases slowly, then there is a convex defect on the metal surface.

[0023] If the temperature gradient-time curve shows a sharp decrease, then a rapid increase to a certain size, and then a slow decrease, then there is a concave defect on the metal surface.

[0024] If the temperature gradient-time curve shows a rapid decrease followed by a slow increase, and then a slow decrease again, then there are defects on the subsurface of the metal.

[0025] Among them, the magnitudes of the monotonically decreasing rate Vd1, the rapidly decreasing rate Vd2, the slowly decreasing rate Vd3, and the sharply decreasing rate Vd4 are related as follows: |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. When the processor executes the computer program, it implements the above-described eddy current thermal imaging defect detection method based on temperature gradient processing.

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

[0029] The present invention also provides a readable storage medium having a computer program stored thereon, which, when executed, implements the above-described eddy current thermal imaging defect detection method based on temperature gradient processing.

[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 subsurface defects. It can reduce the influence of various factors, is especially useful for small defects that are sensitive to the surrounding environment, and has universal applicability to defects of various small sizes and shapes.

[0032] (2) The present invention utilizes the correspondence between pixel position and spatial position, and uses the difference method to estimate the partial derivative of temperature T with respect to x at the center position (x,y) of the metal surface. Since the 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 and reduce the error when capturing images, this invention proposes to calculate the partial derivatives of the temperature T at the center position (x,y) of the metal surface in four directions: (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.

[0034] (4) To facilitate calculation, this 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 devices, computer program products and readable storage media disclosed in this invention can produce the same beneficial effects as the above methods by applying the above methods, and will not be described again. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention.

[0037] Figure 2 This is a schematic diagram of the convolution kernel h(k,l).

[0038] Figure 3 This is a schematic diagram of convolution in the method of the present invention.

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

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1

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

[0043] Step 1: In the thermal imaging camera system, the camera's intrinsic parameter A is calibrated using Zhang Zhengyou's calibration method and a specific calibration plate based on the pinhole camera model. c .

[0044] Step 2: Determine the extrinsic parameters of the thermal imager camera system, i.e., the correspondence between spatial position and pixel position, with the object surface as the plane z=0.

[0045] Step 3: Perform eddy current heating on the defective metal surface and use a thermal imager to capture continuous thermal images of the metal heating and cooling stages.

[0046] Step 4: Select the thermal images of the cooling phase from the captured images, and arrange them 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 Includes temperature data for each pixel in the image; n≥59; selects the pixel (u,v) with the highest temperature in the first frame image (t0,T0), and uses 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 temperature T with respect to x at the center position (x,y) of the metal surface, and reduce the influence of noise to minimize the error.

[0048] Step 5.1: Based on the correspondence between spatial position (x, y) and pixel position (u, v), the formula for calculating the partial derivative of temperature T with respect to x is obtained using the difference derivative method:

[0049]

[0050] In the formula, For t i The partial derivative of temperature T with respect to x at time T;i (x+Δx,y) is t i Temperature data at the metal surface at time (x+Δx,y), i.e., the temperature of the pixel corresponding to the spatial location (x+Δx,y); T i (x,y) is t i Temperature data at (x, y) on the metal surface at any given time, i.e., the temperature of the pixel corresponding to the spatial location (x, y); Δx is the actual size of the image pixel physical size Δu mapped onto 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 (x+Δx,y), (x-Δx,y), (x,y+Δy), and (x,y-Δy). Use their average value as an estimate 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 metals are anisotropic and Δx = Δy, the estimated value of the partial derivative of temperature T with respect to x at the center position (x, y) of the metal surface is:

[0054]

[0055] The result of equation (3) above 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 ease of calculation, this invention can use convolution to calculate the temperature gradient at the center position (x, y) of the metal surface:

[0057]

[0058] 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 (x-1+k,y-1+k) of the metal surface; h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, and k,l=0,1,2;

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

[0060]

[0061] Step 6: Calculate the temperature gradient at the center position (x, y) of the metal surface at each time step according to equation (3). Plot the temperature gradient over time using at least n sets of data. The temperature gradient-time curve is plotted and compared with the theoretical temperature gradient-time curve to determine the type of defect. The theoretical temperature gradient-time curve includes temperature gradient-time curves for various defects and for defect-free conditions.

[0062] If the temperature gradient-time curve shows a monotonically decreasing trend, then the metal has no defects.

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

[0064] If the temperature gradient-time curve shows a sharp decrease followed by a rapid increase to a certain value, and then a slow decrease again, then the metal surface has a concave defect.

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

[0066] 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 figures show the temperature gradient-time curves for various types of defects. Among them, (a) is the temperature gradient-time curve for defects without defects, (b) is the temperature gradient-time curve for convex surface defects, (c) is the temperature gradient-time curve for concave surface defects, and (d) is the temperature gradient-time curve for subsurface defects.

[0069] Example 2

[0070] An electronic device 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, it implements the eddy current thermal imaging defect detection method based on temperature gradient processing described in Embodiment 1 above.

[0071] The electronic device in this application embodiment may be the mobile device itself, or a standalone device independent of it. The standalone device may communicate with the mobile device to receive the collected input signals from it and send the selected target decision behavior to it.

[0072] An electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other processing unit 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. 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 execute the program instructions to implement the decision-making behavior and decision-making methods of the various embodiments of this application described above, and / or other desired functions.

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

[0074] Example 3

[0075] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the decision-making behavior method according to various embodiments of this application as described in Embodiment 1 above.

[0076] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0077] Example 4

[0078] Embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the decision-making behavior decision-making method according to various embodiments of this application described in Embodiment 1 above.

[0079] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

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

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

Claims

1. A defect detection method based on eddy current thermal imaging with temperature gradient processing, characterized in that, Includes the following steps: Step 1: Calibrate the camera intrinsic parameters in the thermal imaging camera system; Step 2: Determine the camera extrinsic parameters in the thermal imager camera system using the object surface as the plane with z=0, and obtain the correspondence between spatial position and pixel position; Step 3: Perform eddy current heating on the metal surface to be tested, and use a thermal imager to capture continuous thermal 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 (u,v) with the highest temperature in the first frame image, 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; Step 5: Estimate the partial derivative of 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: Based on the temperature gradient at the center position (x, y) of the metal surface at each time point, plot the temperature gradient-time curve and compare it with the theoretical temperature gradient-time curve to determine the type of defect; In step 6, the specific method for determining the defect category is as follows: If the temperature gradient-time curve shows a monotonically decreasing trend, then the metal has no defects. If the temperature gradient-time curve first increases rapidly to a certain size, then decreases rapidly and then decreases slowly, then there is a convex defect on the metal surface. If the temperature gradient-time curve shows a sharp decrease, then a rapid increase to a certain size, and then a slow decrease, then there is a concave defect on the metal surface. If the temperature gradient-time curve shows a rapid decrease followed by a slow increase, and then a slow decrease again, then there are defects on the subsurface of the metal. Among them, the magnitudes of the monotonically decreasing rate Vd1, the rapidly decreasing rate Vd2, the slowly decreasing rate Vd3, and the sharply decreasing rate Vd4 are related as follows: |Vd4|>|Vd1|>|Vd2|>|Vd3|>0; The relationship between the rapidly increasing rate Vu1 and the slowly increasing rate Vu2 is: |Vu1|>|Vu2|>0.

2. The eddy current thermal imaging defect detection method based on temperature gradient processing according to claim 1, characterized in that, In step 5, based on the correspondence between spatial location and pixel location, and based on the temperature data surrounding the center location (x, y) of the metal surface, the partial derivatives of the temperature T at the center location (x, y) of the metal surface are calculated in four directions: (x+Δx, y), (x-Δx, y), (x, y+Δy), and (x, y-Δy) using the differential differentiation method. The average of these derivatives yields an estimated value for the partial derivative of the temperature T at the center location (x, y) of the metal surface with respect to x. In the formula, For t i The partial derivatives of temperature T at time T in the four directions (x+Δx,y), (x-Δx,y), (x,y+Δy), and (x,y-Δy); i (x,y) is t i Temperature data at the center position (x, y) of the metal surface at any given time, i.e., the temperature of the pixel corresponding to the spatial position (x, y); T i (x+Δx,y) is t i Temperature data at the metal surface at time (x+Δx,y); T i (x-Δx,y) is t i Temperature data at the metal surface at time (x-Δx,y); T i (x, y + Δy) is t i Temperature data at the metal surface at time (x, y + Δy); T i (x, y - Δy) is t i Temperature data at (x, y - Δy) on the metal surface at time (x, y - Δy); Δx = Δy is the actual size of the metal surface mapped to the pixel physical size Δu of the image; For t i The partial derivative of temperature T with respect to x is estimated, which 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, characterized in that, The formula for calculating the temperature gradient at the center position (x, y) of the metal surface is converted into a convolution form: In the formula, 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 (x-1+k,y-1+k) of the metal surface, h(k,l) is the convolution kernel, k represents the row of the convolution kernel, l represents the column of the convolution kernel, and k,l=0,1,2; The convolution kernel h(k,l) is:

4. 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, it implements the eddy current thermal imaging defect detection method based on temperature gradient processing as described in any one of claims 1-3.

5. A readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the eddy current thermal imaging defect detection method based on temperature gradient processing as described in any one of claims 1-3.

Images