Brake disc welding defect detection method and system

Abstract

The invention relates to the technical field of data processing, and discloses a brake disc welding defect detection method and system, and the method comprises the steps: collecting an original image of a brake disc welding region; performing denoising processing and edge enhancement processing on the original image; extracting the characteristics of the crack and the unfused area based on multi-scale wavelet transform, and preliminarily positioning a defect area; introducing a crack direction weight to optimize high-frequency energy response; generating a brake disc welding defect detection report; compared with the technical problem that welding cracks and incomplete fusion area defects cannot be efficiently and accurately detected under complex welding conditions such as high reflection or complex welding texture conditions in the prior art, the method has the advantages that the multi-scale wavelet transform is combined with the direction weight optimization and the gray level smoothing error correction; the high-precision detection of welding cracks and incomplete fusion defects is realized, so that the problems of low detection precision and high misjudgment rate in a traditional method are avoided, and the detection efficiency and reliability of the welding quality of the brake disc are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a brake disc welding defect detection method and system. Background Art

[0002] At present, the detection of brake disc welding defects mainly relies on manual inspection or traditional automated inspection systems. However, the existing technology has many technical problems in dealing with complex welding defects. First, the manual inspection method is not only inefficient, but also easily affected by the operator's experience and fatigue, resulting in inconsistency in the detection results, making it difficult to ensure the consistency and reliability of welding quality. Second, the traditional automated inspection system is often unable to accurately identify cracks and unfused defects when faced with the complexity of the welding surface, reflective materials and texture changes. The existing image processing methods mostly rely on a single algorithm and lack adaptability to variable welding environments, especially in terms of highly reflective materials, complex welding paths and multi-scale welding defect recognition. The technical bottleneck is obvious, resulting in the inability to achieve high-precision and high-efficiency defect detection. Therefore, the existing technology cannot fully meet the actual needs of brake disc welding quality inspection, especially under conditions such as high reflective environments and complex welding textures. There is still a technical problem that cracks and unfused areas cannot be accurately located. For this reason, there is an urgent need for a method that can still achieve high-precision defect recognition under complex welding conditions to overcome the defects in traditional technologies and improve the accuracy and efficiency of welding quality inspection. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a brake disc welding defect detection method, aiming to solve the technical problem in the prior art that under complex welding conditions, such as high reflection or complex welding texture conditions, it is impossible to achieve efficient and accurate detection of welding cracks and unfused area defects.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a brake disc welding defect detection method,

[0005] The brake disc welding defect detection method comprises:

[0006] Step S10: The original welding area image I(x, y) is obtained by capturing the image of the brake disc welding area with a high-definition industrial camera, wherein x and y are image coordinates, respectively representing the horizontal and vertical coordinates of the pixels of the welding area; the resolution of the industrial camera is more than 3 million pixels, and the frame rate is 30 frames per second;

[0007] Step S20: Based on the original welding area image I(x, y) obtained in step S10, denoising and edge enhancement are performed on it, including:

[0008] Use a Gaussian filter to remove the noise of the original welding area image I(x,y) to obtain the denoised welding area image I g (x,y);

[0009] For the denoised welding area image I g (x, y) is edge enhanced to obtain the welding area enhanced image I e (x,y);

[0010] Step S30: Based on the welding area enhanced image I obtained in step S20 e (x, y), and extract the characteristics of cracks and unfused areas through multi-scale wavelet transform, including:

[0011] The weld area enhanced image is decomposed into low-frequency components L using three-layer wavelet decomposition. i and high frequency component H i , where L i is the low-frequency component of the welding area, which is used to reflect the background smoothness characteristics of the unfused area; H i is the high-frequency component of the welding area, which is used to reflect the linear characteristics of the crack edge; i is the scale of decomposition, which takes a value of 1, 2 or 3;

[0012] Calculate the high frequency energy response E d (x,y), preset high-frequency energy response threshold E t , when the high frequency energy response E d (x,y) is greater than the high-frequency energy response threshold E t When , the pixel at the image coordinate (x, y) is determined to be a crack and unfused defect point pixel, and the crack and unfused defect point pixels are aggregated to obtain the crack and unfused area;

[0013] Step S40: Introducing the crack direction weight P j , the calculation formula is:

[0014] P j =exp(-k·θ j )

[0015] Among them, θ j is the direction angle of the crack, ranging from 0° to 180°; k is the directional attenuation coefficient;

[0016] According to the crack direction weight P j Optimizing high frequency energy response:

[0017] E d ′(x,y)=P j ·E d (x,y)

[0018] Among them, E′ d(x, y) is the high-frequency energy response after directional weight optimization;

[0019] Set the high-frequency energy threshold T of the crack c , if E′ d (x,y)>T c , then the pixel at the image coordinate (x, y) is determined to be a crack region point pixel, and the image coordinates of the crack region point pixel are output;

[0020] Introducing the unfused grayscale smoothing error L u , set the unfused grayscale smoothing threshold T u , if L u <T u , then the pixel at the image coordinate (x, y) is determined to be a pixel at the unfused area point, and the image coordinates of the pixel at the unfused area point are output;

[0021] Step S50: Calculate the overall defect intensity parameter S f , combining the pixel image coordinates of the crack area points and the pixel image coordinates of the unfused area points and the overall defect intensity parameters to generate a brake disc welding inspection report.

[0022] Preferably, in step S20, the denoising formula is:

[0023]

[0024] Among them, σ is the standard deviation of Gaussian filtering, ranging from 1.2 to 1.5, which is used to control the denoising strength; exp is the exponential function.

[0025] Preferably, the formula for edge enhancement processing is:

[0026]

[0027] Among them, α is the edge enhancement weight parameter; is the second-order derivative of the denoised image, which is used to highlight the contrast between the crack edge and the unfused contour.

[0028] Preferably, the high frequency energy response E d The calculation formula for (x,y) is:

[0029]

[0030] Among them, E d (x, y) is the high-frequency energy response at the image coordinate (x, y), reflecting the intensity of the crack edge and the contour intensity of the unfused area; N, M are the width and height of the crack and the unfused area.

[0031] Preferably, in step S50, the overall defect intensity parameter S f The calculation formula is:

[0032]

[0033] Where n, m are the image coordinates of any point in the crack and unfused area, and f n,n is the pixel value in the crack and unfused area; is the background grayscale average of the crack and unfused area; A d is the area of ​​the crack and the unfused area, in square pixels; N and M are the width and height of the crack and the unfused area.

[0034] Preferably, in step S50, the content of the inspection report includes defect type, defect coordinates, defect area and defect intensity parameter S f .

[0035] Preferably, in step S40, the unfused grayscale smoothing error L u The calculation formula is:

[0036]

[0037] Where n, m are the image coordinates of any point in the crack and unfused area, and f n,m is the pixel value in the crack and unfused area; is the background grayscale mean of the crack and unfused area; λ is the smoothing weight parameter, ranging from 0.05 to 0.2; is the second-order derivative of the pixel value in the crack and unfused area.

[0038] The present invention also provides a brake disc welding defect detection system comprising:

[0039] The image acquisition module is used to acquire the image of the brake disc welding area through a high-definition industrial camera to obtain the original welding area image I (x, y), where x and y are image coordinates, representing the horizontal and vertical coordinates of the pixels of the welding area respectively; the resolution of the industrial camera is more than 3 million pixels, and the frame rate is 30 frames per second;

[0040] The image preprocessing module is used to perform denoising and edge enhancement processing on the original welding area image I(x, y) obtained in step S10, including:

[0041] Use a Gaussian filter to remove the noise of the original welding area image I(x,y) to obtain the denoised welding area image I g (x,y);

[0042] For the denoised welding area image I g (x, y) is edge enhanced to obtain the welding area enhanced image I e (x,y);

[0043] A feature extraction module is used to enhance the welding area image I obtained in step S20. e (x, y), and extract the characteristics of cracks and unfused areas through multi-scale wavelet transform, including:

[0044] The weld area enhanced image is decomposed into low-frequency components L using three-layer wavelet decomposition. i and high frequency component H i , where L i is the low-frequency component of the welding area, which is used to reflect the background smoothness characteristics of the unfused area; H i is the high-frequency component of the welding area, which is used to reflect the linear characteristics of the crack edge; i is the scale of decomposition, which takes a value of 1, 2 or 3;

[0045] Calculate the high frequency energy response E d (x,y), preset high-frequency energy response threshold E t , when the high frequency energy response E d (x,y) is greater than the high-frequency energy response threshold E t When , the pixel at the image coordinate (x, y) is determined to be a crack and unfused defect point pixel, and the crack and unfused defect point pixels are aggregated to obtain the crack and unfused area;

[0046] Defect location and weight optimization module, used to introduce crack direction weight P j , the calculation formula is:

[0047] P j =exp(-k·θ j )

[0048] Among them, θ j is the direction angle of the crack, ranging from 0° to 180°; k is the directional attenuation coefficient;

[0049] According to the crack direction weight P j Optimizing high frequency energy response:

[0050] E d ′(x,y)=P j ·E d (x,y)

[0051] Among them, E′ d (x, y) is the high-frequency energy response after directional weight optimization;

[0052] Set the high-frequency energy threshold T of the crack c , if E′ d (x,y)>T c , then the pixel at the image coordinate (x, y) is determined to be a crack region point pixel, and the image coordinates of the crack region point pixel are output;

[0053] Introducing the unfused grayscale smoothing error L u , set the unfused grayscale smoothing threshold T u , if L u <T u , then the pixel at the image coordinate (x, y) is determined to be a pixel at the unfused area point, and the image coordinates of the pixel at the unfused area point are output;

[0054] Defect assessment and report generation module for calculating the overall defect intensity parameter S f , combining the pixel image coordinates of the crack area points and the pixel image coordinates of the unfused area points and the overall defect intensity parameters to generate a brake disc welding inspection report.

[0055] The present invention also provides a computer program product, including a brake disc welding defect detection program, and the brake disc welding defect detection program implements the brake disc welding defect detection method when executed by a processor.

[0056] The beneficial effect of the present invention is that compared with the technical problem in the prior art that under complex welding conditions, such as high reflection or complex welding texture, it is impossible to achieve efficient and accurate detection of welding cracks and unfused area defects, the present invention achieves high-precision detection of welding cracks and unfused defects through multi-scale wavelet transform combined with directional weight optimization and grayscale smoothing error correction, thereby avoiding the problems of low detection accuracy and high misjudgment rate in traditional methods, and greatly improving the efficiency and reliability of brake disc welding quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 The figure is a flow chart of a first embodiment of a method for detecting welding defects of a brake disc according to the present invention.

[0059] Figure 2 The present invention is a schematic diagram of equipment for a brake disc welding defect detection method. DETAILED DESCRIPTION

[0060] 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.

[0061] Embodiment 1: Figure 1 , which is a flow chart of a first embodiment of a method for detecting welding defects in a brake disc according to the present invention, provides a first embodiment of a method for detecting welding defects in a brake disc according to the present invention.

[0062] In a first embodiment, the brake disc welding defect detection method includes:

[0063] Step S10: The original welding area image I(x, y) is obtained by capturing the image of the brake disc welding area with a high-definition industrial camera, wherein x and y are image coordinates, respectively representing the horizontal and vertical coordinates of the pixels of the welding area; the resolution of the industrial camera is more than 3 million pixels, and the frame rate is 30 frames per second;

[0064] It is understandable that the application of high-resolution industrial cameras makes the collected images have high precision and clarity, which can effectively reflect the subtle structure and defect characteristics of the welding area and avoid defect blur or missed detection due to insufficient resolution. In addition, the frame rate of 30 frames per second ensures the real-time and continuity of image acquisition, which is particularly suitable for dynamic detection scenarios and is conducive to the stability and accuracy of subsequent image processing. For example, in the case of tiny cracks or unfused defects in the welding area, the original images collected by the industrial camera can accurately present the edges, shapes and distribution characteristics of the defects, providing high-quality input data for subsequent denoising, feature extraction and defect recognition.

[0065] Step S20: Based on the original welding area image I(x, y) obtained in step S10, denoising and edge enhancement are performed on it, including:

[0066] Use a Gaussian filter to remove the noise of the original welding area image I(x,y) to obtain the denoised welding area image I g (x,y);

[0067] For the denoised welding area image I g (x, y) is edge enhanced to obtain the welding area enhanced image I e (x,y);

[0068] It should be noted that in step S20, the denoising formula is:

[0069]

[0070] Among them, σ is the standard deviation of Gaussian filtering, ranging from 1.2 to 1.5, which is used to control the denoising strength; exp is the exponential function.

[0071] In step S20, the formula for edge enhancement processing is:

[0072]

[0073] Among them, α is the edge enhancement weight parameter; is the second-order derivative of the denoised image, which is used to highlight the contrast between the crack edge and the unfused contour.

[0074] It can be understood that the original welding area image is denoised by Gaussian filtering to eliminate noise interference and obtain the denoised welding area image. The application of Gaussian filtering can effectively smooth the image, remove the influence of random noise, ensure that the background of the welding area is more uniform, and facilitate the subsequent accurate extraction of defect edges; on the basis of the denoised image, the edge enhancement processing is used to further highlight the defect contour features in the welding area and enhance the edge contrast between the crack and the unfused area. This processing makes the difference between the crack edge and the background area more obvious by amplifying the edge information of the image, thereby improving the recognizability of welding defects.

[0075] Through the denoising and edge enhancement processing in this step, the noise in the welding area image is effectively removed, and the edge details of defects such as cracks and lack of fusion are significantly enhanced, ensuring the clarity of the image and the accuracy of feature expression, laying the foundation for the subsequent precise positioning and identification of defects. Compared with the existing technology, this step not only improves the image quality, but also ensures the accuracy and reliability of welding defect detection under complex welding backgrounds.

[0076] Step S30: Based on the welding area enhanced image I obtained in step S20 e (x, y), and extract the characteristics of cracks and unfused areas through multi-scale wavelet transform, including:

[0077] The weld area enhanced image is decomposed into low-frequency components L using three-layer wavelet decomposition. i and high frequency component H i , where L i is the low-frequency component of the welding area, which is used to reflect the background smoothness characteristics of the unfused area; H i is the high-frequency component of the welding area, which is used to reflect the linear characteristics of the crack edge; i is the scale of decomposition, which takes a value of 1, 2 or 3;

[0078] Calculate the high frequency energy response E d (x,y), preset high-frequency energy response threshold E t , when the high frequency energy response E d(x,y) is greater than the high-frequency energy response threshold E t When , the pixel at the image coordinate (x, y) is determined to be a crack and unfused defect point pixel, and the crack and unfused defect point pixels are aggregated to obtain the crack and unfused area;

[0079] It should be noted that this step uses multi-scale wavelet transform to effectively separate the background information and defect characteristics of the welding area, and accurately extracts the edge features of cracks and unfused areas. Compared with the problem of insufficient defect recognition ability under high reflection and complex background in the prior art, this step can accurately identify and locate the welding defect area while ensuring the image details, providing a reliable basis for subsequent defect intensity analysis and report generation.

[0080] Step S40: Introducing the crack direction weight P j , the calculation formula is:

[0081] P j =exp(-k·θ j )

[0082] Among them, θ j is the direction angle of the crack, ranging from 0° to 180°; k is the directional attenuation coefficient;

[0083] According to the crack direction weight P j Optimizing high frequency energy response:

[0084] E d ′(x,y)=P j ·E d (x,y)

[0085] Among them, E′ d (x, y) is the high-frequency energy response after directional weight optimization;

[0086] Set the high-frequency energy threshold T of the crack c , if E′ d (x,y)>T c , then the pixel at the image coordinate (x, y) is determined to be a crack region point pixel, and the image coordinates of the crack region point pixel are output;

[0087] Introducing the unfused grayscale smoothing error L u , set the unfused grayscale smoothing threshold T u , if L u <T u , then the pixel at the image coordinate (x, y) is determined to be a pixel at the unfused area point, and the image coordinates of the pixel at the unfused area point are output;

[0088] It should be understood that by introducing the crack direction weight and the unfused grayscale smoothing error, this step can achieve high-precision identification of welding defect areas. The crack direction weight effectively enhances the energy response of the crack in a specific direction and significantly improves the accuracy of crack detection. The setting of the grayscale smoothing error accurately determines the smooth background features of the unfused area, avoiding the misjudgment of the unfused area in the traditional method. Compared with the prior art, this step can accurately extract the edges and features of cracks and unfused areas under complex welding conditions, ensure the stability and accuracy of defect identification, and provide high-quality coordinate data and basis for subsequent defect evaluation.

[0089] Step S50: Calculate the overall defect intensity parameter S f , combining the pixel image coordinates of the crack area points and the pixel image coordinates of the unfused area points and the overall defect intensity parameters to generate a brake disc welding inspection report.

[0090] It should be noted that the overall defect intensity parameter S f The calculation formula is:

[0091]

[0092] Among them, A d The area of ​​cracks and lack of fusion in square pixels.

[0093] It should be understood that by calculating the overall defect intensity parameter, the defect distribution and intensity of the welding area can be quantified, reflecting the severity of defects in cracks and unfused areas. The overall defect intensity parameter makes the detection result more objective and accurate by statistically analyzing the pixel intensity deviation in the region and combining the regional area information. Compared with the prior art, this step can not only quantitatively evaluate the distribution and intensity of defects, but also provide a complete welding quality assessment report, ensure the visualization and dataization of defect detection, and provide a reliable basis for welding quality control.

[0094] In addition, the brake disc welding defect detection system provided by the present invention adopts a brake disc welding defect detection method in the above embodiment, which can solve the technical problem of brake disc welding defect detection. Compared with the prior art, the beneficial effects of the brake disc welding defect detection system provided by the present invention are the same as the beneficial effects of the brake disc welding defect detection method provided by the above embodiment, and other technical features of the brake disc welding defect detection system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0095] The present invention provides a brake disc welding defect detection device, please refer to Figure 2, a brake disc welding defect detection device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a brake disc welding defect detection method in the above-mentioned embodiment 1. A brake disc welding defect detection device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A brake disc welding defect detection device is only an example and should not bring any limitations to the functions and scope of use of the embodiments of the present invention. A brake disc welding defect detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of a brake disc welding defect detection device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a brake disc welding defect detection device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a brake disc welding defect detection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0096] The present invention also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for detecting welding defects of a brake disc when the computer program is executed by a processor. The computer program product provided by the present invention can solve the technical problem of detecting welding defects of a brake disc. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for detecting welding defects of a brake disc provided by the above-mentioned embodiment, which will not be described in detail here.

[0097] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0098] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0099] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A brake disc welding defect detection method, characterized in that: Methods include: Step S10: The original welding area image I(x, y) is obtained by capturing the image of the brake disc welding area with a high-definition industrial camera, wherein x and y are image coordinates, respectively representing the horizontal and vertical coordinates of the pixels of the welding area; the resolution of the industrial camera is more than 3 million pixels, and the frame rate is 30 frames per second; Step S20: Based on the original welding area image I(x, y) obtained in step S10, denoising and edge enhancement are performed on it, including: Use a Gaussian filter to remove the noise of the original welding area image I(x,y) to obtain the denoised welding area image I g (x,y); For the denoised welding area image I g (x, y) is edge enhanced to obtain the welding area enhanced image I e (x,y); Step S30: Based on the welding area enhanced image I obtained in step S20 e (x, y), and extract the characteristics of cracks and unfused areas through multi-scale wavelet transform, including: The weld area enhanced image is decomposed into low-frequency components L using three-layer wavelet decomposition. i and high frequency component H i , where L i is the low-frequency component of the welding area, which is used to reflect the background smoothness characteristics of the unfused area; H i is the high-frequency component of the welding area, which is used to reflect the linear characteristics of the crack edge; i is the scale of decomposition, which takes a value of 1, 2 or 3; Calculate the high frequency energy response E d (x,y), preset high-frequency energy response threshold E t , when the high frequency energy response E d (x,y) is greater than the high-frequency energy response threshold E t When , the pixel at the image coordinate (x, y) is determined to be a crack and unfused defect point pixel, and the crack and unfused defect point pixels are aggregated to obtain the crack and unfused area; Step S40: Introducing the crack direction weight P j , the calculation formula is: P j =exp(-k·θ j ) Among them, θ j is the direction angle of the crack, ranging from 0° to 180°; k is the directional attenuation coefficient; According to the crack direction weight P j Optimizing high frequency energy response: AND d ′(x,y)=P j ·AND d (x,y) Among them, E′ d (x, y) is the high-frequency energy response after directional weight optimization; Set the high-frequency energy threshold T of the crack c , if E′ d (x,y)>T c , then the pixel at the image coordinate (x, y) is determined to be a crack region point pixel, and the image coordinates of the crack region point pixel are output; Introducing the unfused grayscale smoothing error L u , set the unfused grayscale smoothing threshold T u , if L u <T u , then the pixel at the image coordinate (x, y) is determined to be a pixel at the unfused area point, and the image coordinates of the pixel at the unfused area point are output; Step S50: Calculate the overall defect intensity parameter S f , combining the pixel image coordinates of the crack area points and the pixel image coordinates of the unfused area points and the overall defect intensity parameters to generate a brake disc welding inspection report.

2. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S20, the denoising formula is: Among them, σ is the standard deviation of Gaussian filtering, ranging from 1.2 to 1.5, which is used to control the denoising strength; exp is the exponential function.

3. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S20, the formula for edge enhancement processing is: Among them, α is the edge enhancement weight parameter; is the second-order derivative of the denoised image, which is used to highlight the contrast between the crack edge and the unfused contour.

4. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S30, the high frequency energy response E d The calculation formula for (x,y) is: Among them, E d (x, y) is the high-frequency energy response at the image coordinate (x, y), which reflects the intensity of the crack edge and the contour intensity of the unfused area.

5. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S50, the overall defect intensity parameter S f The calculation formula is: Where n, m are the image coordinates of any point in the crack and unfused area, and f n,m is the pixel value in the crack and unfused area; is the background grayscale average of the crack and unfused area; A d is the area of ​​the crack and the unfused area, in square pixels; N and M are the width and height of the crack and the unfused area.

6. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S50, the content of the inspection report includes defect type, defect coordinates, defect area and defect intensity parameter S f .

7. A brake disc welding defect detection method as claimed in claim 1, characterized in that: In step S40, the unfused grayscale smoothing error L u The calculation formula is: Where n, m are the image coordinates of any point in the crack and unfused area, and f n,m is the pixel value in the crack and unfused area; is the background grayscale mean of the crack and unfused area; λ is the smoothing weight parameter, ranging from 0.05 to 0.2; is the second-order derivative of the pixel value in the crack and unfused area.

8. A brake disc welding defect detection system, characterized in that: The brake disc welding defect detection system comprises: The image acquisition module is used to acquire the image of the brake disc welding area through a high-definition industrial camera to obtain the original welding area image I (x, y), where x and y are image coordinates, representing the horizontal and vertical coordinates of the pixels of the welding area respectively; the resolution of the industrial camera is more than 3 million pixels, and the frame rate is 30 frames per second; The image preprocessing module is used to perform denoising and edge enhancement processing on the original welding area image I(x, y) obtained in step S10, including: Use a Gaussian filter to remove the noise of the original welding area image I(x,y) to obtain the denoised welding area image I g (x,y); For the denoised welding area image I g (x, y) is edge enhanced to obtain the welding area enhanced image I e (x,y); A feature extraction module is used to enhance the welding area image I obtained in step S20. e (x, y), and extract the characteristics of cracks and unfused areas through multi-scale wavelet transform, including: The weld area enhanced image is decomposed into low-frequency components L using three-layer wavelet decomposition. i and high frequency component H i , where L i is the low-frequency component of the welding area, which is used to reflect the background smoothness characteristics of the unfused area; H i is the high-frequency component of the welding area, which is used to reflect the linear characteristics of the crack edge; i is the scale of decomposition, which takes a value of 1, 2 or 3; Calculate the high frequency energy response E d (x,y), preset high-frequency energy response threshold E t , when the high frequency energy response E d (x,y) is greater than the high-frequency energy response threshold E t When , the pixel at the image coordinate (x, y) is determined to be a crack and unfused defect point pixel, and the crack and unfused defect point pixels are aggregated to obtain the crack and unfused area; Defect location and weight optimization module, used to introduce crack direction weight P j , the calculation formula is: P j =exp(-k·θ j ) Among them, θ j is the direction angle of the crack, ranging from 0° to 180°; k is the directional attenuation coefficient; According to the crack direction weight P j Optimizing high frequency energy response: AND d ′(x,y)=P j ·AND d (x,y) Among them, E′ d (x, y) is the high-frequency energy response after directional weight optimization; Set the high-frequency energy threshold T of the crack c , if E′ d (x,y)>T c , then the pixel at the image coordinate (x, y) is determined to be a crack region point pixel, and the image coordinates of the crack region point pixel are output; Introducing the unfused grayscale smoothing error L u , set the unfused grayscale smoothing threshold T u , if L u <T u , then the pixel at the image coordinate (x, y) is determined to be a pixel at the unfused area point, and the image coordinates of the pixel at the unfused area point are output; Defect assessment and report generation module for calculating the overall defect intensity parameter S f , combining the pixel image coordinates of the crack area points and the pixel image coordinates of the unfused area points and the overall defect intensity parameters to generate a brake disc welding inspection report.

9. A brake disc welding defect detection device, characterized in that: The brake disc welding defect detection device includes: a memory, a processor, and a brake disc welding defect detection program stored in the memory and executable on the processor. When the brake disc welding defect detection program is executed by the processor, the brake disc welding defect detection method described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a brake disc welding defect detection program, which, when executed by a processor, implements the brake disc welding defect detection method described in any one of claims 1 to 7.

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