Copper foil surface defect detection method, device, computer equipment and storage medium

Abstract

The present application relates to the technical field of copper foil inspection and provides a copper foil surface defect inspection method, apparatus, computer equipment, and storage medium. The present application adjusts the real-time acquisition speed of a camera based on the real-time movement speed of the copper foil to be inspected and a preset baseline acquisition speed, and acquires images of the upper and lower surfaces of the copper foil to be inspected; obtains corresponding feature point pairs in the upper and lower surface images; identifies potential defect areas in the upper and lower surface images based on the corresponding feature point pairs; performs surface defect inspection on the potential defect areas to obtain hidden defect detection results; performs surface defect inspection on the upper and lower surface images to obtain upper and lower surface defect detection results; and generates copper foil surface defect detection results based on the hidden defect detection results and the upper and lower surface defect detection results. The present application can simultaneously inspect the upper and lower surfaces of the copper foil and hidden defects, thereby improving the accuracy and comprehensiveness of the inspection results.

Description

Technical Field

[0001] The present application relates to the field of copper foil detection technology, and in particular to a method, device, computer equipment and storage medium for detecting copper foil surface defects. Background Art

[0002] In the copper foil production process, surface defects are one of the most important indicators of its quality and performance. With the rapid development of image processing technology, automated copper foil surface defect detection systems based on this technology have emerged, which not only reduces the burden of manual inspection but also improves inspection efficiency.

[0003] However, most existing copper foil inspection methods based on image processing technology focus solely on a single surface image of the copper foil. Specifically, they perform defect detection on either the top or bottom surface images, ignoring any correlations between the top and bottom surfaces. This single-view inspection method fails to fully capture all defect characteristics on the copper foil surface, thus limiting the accuracy and comprehensiveness of the inspection results. Summary of the Invention

[0004] Based on this, a copper foil surface defect detection method, device, computer equipment and storage medium are proposed, aiming to solve the technical problem that the existing technology is based on a single-view detection method and cannot fully capture all defects on the copper foil surface.

[0005] A first aspect of the present application provides a method for detecting surface defects of copper foil, the method comprising:

[0006] Adjusting the real-time acquisition speed of the first and second linear array CCD cameras according to the real-time moving speed of the copper foil to be tested and the preset reference acquisition speed;

[0007] Acquire an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and acquire a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed;

[0008] Acquire corresponding feature point pairs in the upper surface image and the lower surface image;

[0009] identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs;

[0010] Performing surface defect detection on the potential defect area to obtain hidden defect detection results;

[0011] Performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result;

[0012] A copper foil surface defect detection result is generated according to the hidden defect detection result, the upper surface defect detection result, and the lower surface defect detection result.

[0013] Optionally, adjusting the real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and a preset reference acquisition speed includes:

[0014] Calculating a moving speed ratio according to the real-time moving speed of the copper foil to be tested and a preset reference moving speed;

[0015] Determining the quality grade according to the model parameters of the copper foil to be tested;

[0016] Obtaining the micro-texture change rate of the copper foil to be tested;

[0017] The real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras are adjusted according to the moving speed ratio, the quality level, the micro texture change rate and the preset reference acquisition speed.

[0018] Optionally, acquiring corresponding feature point pairs in the upper surface image and the lower surface image includes:

[0019] Performing feature point detection on the upper surface image to obtain a first key feature point;

[0020] Performing feature point detection on the lower surface image to obtain second key feature points;

[0021] Feature matching is performed on the first key feature point and the second key feature point to obtain corresponding feature point pairs.

[0022] Optionally, identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs includes:

[0023] Calculating the displacement vector between the corresponding feature point pairs;

[0024] determining whether there is a displacement between the upper surface image and the lower surface image according to the displacement vector;

[0025] When it is determined that there is displacement, the area corresponding to the displacement vector in the upper surface image and the lower surface image is identified as a potential defect area.

[0026] Optionally, performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result includes:

[0027] performing a difference process on the upper surface image and a reference upper surface image to obtain a first difference image;

[0028] Performing surface defect detection based on the upper surface image to obtain a first upper surface defect detection result;

[0029] Performing surface defect detection based on the first differential image to obtain a second upper surface defect detection result;

[0030] Correcting the first upper surface defect detection result according to the second upper surface defect detection result to obtain the upper surface defect detection result;

[0031] performing a differential process on the lower surface image and a reference lower surface image to obtain a second differential image;

[0032] Performing surface defect detection based on the lower surface image to obtain a first lower surface defect detection result;

[0033] performing surface defect detection based on the second differential image to obtain a second lower surface defect detection result;

[0034] The first lower surface defect detection result is corrected according to the second lower surface defect detection result to obtain the lower surface defect detection result.

[0035] Optionally, the method further includes:

[0036] Determining the defect location according to the copper foil surface defect detection result;

[0037] The defect position is sent to a labeling machine, so that the labeling machine labels an area corresponding to the defect position on the copper foil to be tested.

[0038] Optionally, the first group of linear array CCD cameras is used to capture the upper surface of the copper foil to be tested to obtain the upper surface image under the lighting environment provided by the upper surface detection light source and the high-brightness backlight source; the second group of linear array CCD cameras is used to capture the lower surface of the copper foil to be tested to obtain the lower surface image under the lighting environment provided by the lower surface detection light source.

[0039] A second aspect of the present application provides a copper foil surface defect detection device, the device comprising:

[0040] A camera adjustment module is used to adjust the real-time acquisition speed of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and the preset reference acquisition speed;

[0041] an image acquisition module, configured to acquire an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and to acquire a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed;

[0042] A feature acquisition module, configured to acquire corresponding feature point pairs in the upper surface image and the lower surface image;

[0043] an area recognition module, configured to recognize potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs;

[0044] A first detection module is used to perform surface defect detection on the potential defect area to obtain a hidden defect detection result;

[0045] a second detection module, configured to perform surface defect detection on the upper surface image to obtain an upper surface defect detection result, and to perform surface defect detection on the lower surface image to obtain a lower surface defect detection result;

[0046] The result generating module is used to generate the copper foil surface defect detection result according to the hidden defect detection result, the upper surface defect detection result and the lower surface defect detection result.

[0047] A third aspect of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the copper foil surface defect detection method are implemented.

[0048] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the copper foil surface defect detection method are implemented.

[0049] The present embodiment dynamically adjusts the real-time acquisition speeds of the first and second linear array CCD cameras based on the real-time movement speed of the copper foil under test and a preset baseline acquisition speed. This dynamic adjustment mechanism ensures that the cameras consistently capture images at the optimal frame rate, avoiding image blur or frame dropouts. This improves the quality of copper foil surface image acquisition and avoids unnecessary image acquisition and storage, reducing the waste of computing resources and storage space. Using two linear array CCD cameras to capture images of the upper and lower surfaces of the copper foil, respectively, ensures that both are synchronized in time, enabling the inspection process to cover the entire surface of the copper foil and improving comprehensiveness. An image processing algorithm is used to obtain corresponding feature point pairs in the upper and lower surface images. These feature point pairs can accurately capture subtle changes in the upper and lower surfaces of the copper foil, allowing for more accurate identification of potential defect areas. Surface defect detection is performed on the potential defect areas, upper and lower surface images, and the resulting hidden defect detection results, upper surface defect detection results, and lower surface defect detection results are generated. This multi-dimensional inspection method enables a more comprehensive detection of various defect types on the copper foil surface, improving detection accuracy and reliability. Finally, based on the hidden defect detection results, upper surface defect detection results, and lower surface defect detection results, the copper foil surface defect detection results are generated comprehensively. The comprehensive detection results can fully reflect the quality status of the copper foil surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 It is a flow chart of the copper foil surface defect detection method provided in an embodiment of the present application.

[0052] Figure 2 This is a schematic diagram of the interface for copper foil surface defect detection provided in an embodiment of the present application.

[0053] Figure 3 This is a functional module diagram of the copper foil surface defect detection device provided in an embodiment of the present application.

[0054] Figure 4 It is a structural diagram of the computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] Figure 1 This is a flow chart of a copper foil surface defect detection method provided in an embodiment of the present application. The copper foil surface defect detection method includes the following steps.

[0057] S11 , adjusting the real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and a preset reference acquisition speed.

[0058] The copper foil under test is the material requiring surface defect inspection. During the copper foil production process, defects or contamination may occur on both the top and bottom surfaces of the copper foil. Therefore, defect inspection requires simultaneous inspection of both surfaces. The first set of linear array CCD cameras captures images of the top surface of the copper foil under test, while the second set captures images of the bottom surface.

[0059] In an optional embodiment, the first group of linear array CCD cameras is used to capture the upper surface of the copper foil to be tested to obtain the upper surface image under the lighting environment provided by the upper surface detection light source and the high-brightness backlight source; the second group of linear array CCD cameras is used to capture the lower surface of the copper foil to be tested to obtain the lower surface image under the lighting environment provided by the lower surface detection light source.

[0060] During the copper foil production process, the foil moves through the inspection system at a constant speed. This speed may vary or remain constant, and the foil's movement speed (real-time movement speed) needs to be captured in real time. Speed sensors or encoders can be installed at appropriate locations along the foil's path to monitor the foil's movement speed on the production line. These speed sensors can be photoelectric sensors, laser sensors, and other devices.

[0061] The preset baseline acquisition speed is set according to the optimal performance or detection requirements of the system design. It is the default acquisition speed of the system when the actual movement speed of the copper foil is not taken into account.

[0062] The real-time acquisition speed of a linear array CCD camera refers to the number of frames per second (FPS) it can capture. The relationship between the camera's acquisition speed and the copper foil's movement speed directly affects the surface image quality and information integrity. When the camera's acquisition speed is too slow and the copper foil's movement speed is too fast, the camera's shutter remains open for too long, and the copper foil has already moved a certain distance during this time, resulting in image smearing or blurring. Furthermore, due to the slow acquisition speed, the camera may not capture all details of the copper foil's movement, resulting in information loss. When the camera's acquisition speed is too fast and the copper foil's movement speed is too slow, the camera captures a large number of images in a short period of time, many of which may be similar or repetitive, resulting in a significant waste of data storage space and processing resources. Therefore, in order to obtain high-quality images and complete information while saving storage space and processing resources, the real-time acquisition speeds of the first and second linear array CCD cameras need to be adjusted based on the real-time movement speed of the copper foil to be measured and the preset baseline acquisition speed.

[0063] In an optional embodiment, adjusting the real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be tested and the preset reference acquisition speed includes:

[0064] Calculating a moving speed ratio according to the real-time moving speed of the copper foil to be tested and a preset reference moving speed;

[0065] Determining the quality grade according to the model parameters of the copper foil to be tested;

[0066] Obtaining the micro-texture change rate of the copper foil to be tested;

[0067] The real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras are adjusted according to the moving speed ratio, the quality level, the micro texture change rate and the preset reference acquisition speed.

[0068] When the production line is running stably and the copper foil quality is stable, a speed sensor is used to measure and record the average movement speed of the copper foil over a period of time, and this speed is used as the benchmark movement speed. The benchmark movement speed represents the standard or expected movement speed on the production line. The preset benchmark acquisition speed is the system's default acquisition speed when factors such as the actual movement speed, quality grade, and microtexture change rate of the copper foil are not considered. Based on the performance parameters of the linear array CCD camera (such as maximum frame rate, resolution, etc.), a matching benchmark acquisition speed (F_benchmark) is determined. The benchmark acquisition speed matches the performance of the linear array CCD camera to ensure that high-quality images can be captured at the benchmark movement speed.

[0069] During production line operation, the copper foil's real-time speed is monitored by a speed sensor or encoder and transmitted to the camera control system. The control system calculates the ratio between the copper foil's real-time speed and the reference speed. This ratio reflects the difference between the foil's actual speed and the system's intended speed (reference speed). If the ratio is greater than 1, the real-time speed is faster than the reference speed; if the ratio is less than 1, the real-time speed is slower than the reference speed.

[0070] According to the model parameters of the copper foil to be tested, the corresponding database can be searched to determine the quality grade and micro texture change rate of the copper foil to be tested.

[0071] Quality grade refers to the degree to which a product of the same type meets product quality standards. Copper foils of different quality grades vary in physical and chemical properties, such as hardness, strength, corrosion resistance, and conductivity. These differences can cause the copper foil to exhibit varying reflectivity, transmittance, or scattering properties during image acquisition, affecting image clarity and contrast. High-quality copper foil typically has a smoother surface, reducing image distortion or noise caused by surface irregularities. Conversely, low-quality copper foil may cause blurring or distortion in images due to surface irregularities.

[0072] The microtexture change rate is an indicator that describes the speed of microtexture changes on the copper foil surface under test. Copper foil with a high microtexture change rate exhibits a more complex and variable surface texture, which can make it difficult to accurately capture and identify these texture features during image acquisition. Copper foil with a high microtexture change rate may also produce more noise during image acquisition, such as surface defects, scratches, and contamination. This noise reduces image clarity and contrast, making it difficult to accurately reflect the actual condition of the copper foil.

[0073] Therefore, different types of copper foil have different quality grades and microtexture change rates, which significantly affect the quality of image acquisition. Therefore, when capturing images of the upper and lower surfaces of the copper foil, it is necessary to adjust the real-time acquisition speeds of the first and second linear array CCD cameras simultaneously based on the movement speed ratio, quality grade, microtexture change rate, and the preset baseline acquisition speed.

[0074] In an optional embodiment, the real-time acquisition speed can be calculated using the following formula:

[0075] V =V base × R × P × (1 + γ·M);

[0076] Where V is the real-time acquisition speed of the linear array CCD camera, V is the preset baseline acquisition speed, R is the mobile speed ratio, P is the quality level, M is the microtexture change rate, and γ is the influence coefficient of the microtexture change rate, which is determined based on experience. The influence coefficient reflects the sensitivity of the microtexture change rate to acquisition speed adjustments.

[0077] After calculating the real-time acquisition speed of the cameras (the first and second linear array CCD cameras), you can adjust the camera's internal parameters (such as shutter speed and frame rate) to achieve this change. After adjusting the camera's real-time acquisition speed, you can first capture images over a period of time and determine whether the images are clear, stable, and without missing any key information. If you find that the image quality is poor or information is missing, you need to adjust the real-time acquisition speed again.

[0078] The above-mentioned optional embodiment, by monitoring the speed ratio between the real-time movement speed of the copper foil and the reference movement speed and adjusting the real-time acquisition speed based on this ratio, can ensure that the camera acquisition speed matches the movement speed of the copper foil, thereby improving acquisition efficiency and avoiding image overlap or loss. Adjusting the camera acquisition speed according to the quality grade and microtexture change rate of the copper foil can ensure that high-quality images can be obtained on copper foils of different quality grades and texture complexities. For high-quality copper foils, clearer images can be captured; for copper foils with high microtexture change rates, it can ensure that the image can capture more details. This optional embodiment enhances the adaptability of the image acquisition system to copper foils of different models, different quality grades, and different microtexture change rates. By dynamically adjusting the acquisition speed, the system can automatically adapt to different copper foil characteristics without human intervention, improving the level of automation of the entire image acquisition process, helping to reduce production costs, improve production efficiency, and reduce the possibility of human error.

[0079] S12, acquiring an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and acquiring a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed.

[0080] In large-scale, high-efficiency production line inspection scenarios, linear array CCD (Charge-Coupled Device) cameras can quickly capture images of continuously moving objects. In this embodiment, a first set of linear array CCD cameras is used to capture the upper surface of the copper foil under test, obtaining an upper surface image. Simultaneously, a second set of linear array CCD cameras is used to capture the lower surface of the copper foil under test, obtaining a lower surface image.

[0081] S13: Acquire corresponding feature point pairs in the upper surface image and the lower surface image.

[0082] From the perspective of the physical properties of copper foil, its upper and lower surfaces are not completely independent. As a whole, the internal stress and microstructure of the copper foil may affect the morphology of the upper and lower surfaces. For example, during the production process, the copper foil may be subjected to forces such as tension, compression, or bending, which may cause a certain correlation in the morphology of the upper and lower surfaces. In addition, the microstructure of the copper foil (such as grain size and arrangement) may also affect the image characteristics of the upper and lower surfaces. Therefore, it is necessary to obtain corresponding feature point pairs in the upper and lower surface images.

[0083] Feature points are points in an image with unique properties, such as corners, edges, or textured areas. Feature point detection algorithms can be used to detect feature points in the upper surface image and feature points in the lower surface image, thereby generating feature point pairs.

[0084] Among them, feature point detection algorithms may include: Harris corner detection, SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), etc.

[0085] In an optional embodiment, obtaining corresponding feature point pairs in the upper surface image and the lower surface image includes:

[0086] Performing feature point detection on the upper surface image to obtain a first key feature point;

[0087] Performing feature point detection on the lower surface image to obtain second key feature points;

[0088] Feature matching is performed on the first key feature point and the second key feature point to obtain corresponding feature point pairs.

[0089] A feature point detection algorithm is used to detect feature points, such as edges, corners, and texture features, in the upper surface image to obtain a plurality of first key feature points. The same feature point detection algorithm is used to detect feature points corresponding to the upper surface image in the lower surface image to obtain a plurality of second key feature points.

[0090] Using a feature matching algorithm, multiple first key feature points are matched with multiple second key feature points. The matching process involves calculating the similarity between feature descriptors (e.g., Euclidean distance, Hamming distance, etc.) and selecting the point pairs with the highest similarity as matching pairs. The result is a set of corresponding feature point pairs that correspond to the same physical locations or features in both the upper and lower surface images.

[0091] S14 , identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs.

[0092] In an optional embodiment, identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs includes:

[0093] Calculating the displacement vector between the corresponding feature point pairs;

[0094] determining whether there is a displacement between the upper surface image and the lower surface image according to the displacement vector;

[0095] When it is determined that there is displacement, the area corresponding to the displacement vector in the upper surface image and the lower surface image is identified as a potential defect area.

[0096] For each pair of matched feature points, a displacement vector is calculated between the top and bottom surface images. This displacement vector represents the change in position from the feature point in the top surface image to the corresponding feature point in the bottom surface image. Statistical analysis is performed on the calculated displacement vectors, including calculation of the mean, variance, maximum and minimum values, and directional distribution of the vectors. This statistical information helps reveal possible overall deformation or local defects between the top and bottom surfaces of the copper foil. Based on the statistical characteristics of the displacement vectors, one or more thresholds are set to determine whether a feature point pair indicates a potential defect area. For example, a maximum allowable displacement vector value can be set, exceeding which a feature point pair may indicate a defect. This is because, if the copper foil does not deform or move abnormally during transport, the corresponding feature points on the top and bottom surfaces should theoretically maintain a fixed relative position. An abnormal increase in the displacement vector may indicate defects such as bending, twisting, or internal stress in the copper foil. These defects may not be apparent when observing the top or bottom surface alone, but can be revealed by analyzing the correlation between the top and bottom surfaces.

[0097] For pairs of feature points indicating potential defects, their locations in the upper and lower surface images are marked. These locations may form one or more continuous regions, which are initially considered potential defect areas. Specifically, for displaced feature points, a region is marked in each of the upper and lower surface images as a potential defect area, centered around the displaced feature point and based on the magnitude and direction of the displacement vector. A potential defect area is an actual region that should theoretically appear in the lower surface image after the feature point in the upper surface image has been displaced, but is offset from the actual location.

[0098] S15, performing surface defect detection on the potential defect area to obtain a hidden defect detection result.

[0099] Because the marked potential defect areas are determined based on the displacement of feature points, they may not accurately correspond to the actual defect location or shape. Therefore, more sophisticated defect detection algorithms are needed to verify these potential defect areas and determine whether there are actual defects, as well as the specific type and severity of the defects.

[0100] A hidden defect detection model can be pre-trained based on machine learning, and the potential defect area can be used as the input of the hidden defect detection model. The hidden defect detection model is used to perform detection and output the hidden defect detection results.

[0101] S16, performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result.

[0102] While hidden defect detection results provide information about potential defect areas, they may not capture defects in non-potential defect areas. Complete surface defect inspection of both the top and bottom surfaces ensures that all possible defects are detected, providing a more comprehensive quality assessment.

[0103] In an optional embodiment, performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result includes:

[0104] performing a difference process on the upper surface image and a reference upper surface image to obtain a first difference image;

[0105] Performing surface defect detection based on the upper surface image to obtain a first upper surface defect detection result;

[0106] Performing surface defect detection based on the first differential image to obtain a second upper surface defect detection result;

[0107] Correcting the first upper surface defect detection result according to the second upper surface defect detection result to obtain the upper surface defect detection result;

[0108] performing a differential process on the lower surface image and a reference lower surface image to obtain a second differential image;

[0109] Performing surface defect detection based on the lower surface image to obtain a first lower surface defect detection result;

[0110] performing surface defect detection based on the second differential image to obtain a second lower surface defect detection result;

[0111] The first lower surface defect detection result is corrected according to the second lower surface defect detection result to obtain the lower surface defect detection result.

[0112] The reference upper and lower surface images are images of a known, defect-free copper foil surface with a standard appearance. These images serve as a basis for comparison to identify any deviations or defects on the upper and lower surfaces of the copper foil under test. Copper foil surface defects are any physical features on the upper and lower surfaces of the copper foil that do not meet expected standards or specifications, such as scratches, dents, cracks, stains, and oxidation. These defects may affect the performance, reliability, and service life of the copper foil.

[0113] By comparing the top / bottom surface images of the copper foil under test with known reference top / bottom surface images, any deviations from the standard or expectations, which often indicate potential defects, can be more easily identified. Using the reference top / bottom surface images as a reference can reduce false positives and negatives caused by factors such as lighting variations, texture differences, or imaging equipment errors. The reference top / bottom surface images provide a stable basis for comparison, helping to distinguish true defects from false ones.

[0114] Since differential processing can highlight the differences between the two images, it helps to identify defects on the surface of the copper foil. In addition, differential processing has a certain robustness to interference factors such as lighting changes and shadows. This embodiment calculates the pixel differences between the upper / lower surface image and the reference upper / lower surface image to obtain a new image, namely a differential image. The grayscale values or color values of the corresponding pixels in the upper / lower surface image and the reference upper / lower surface image can be subtracted to obtain a differential image. The value of each pixel in the differential image represents the degree of difference between the two images at that position. The differential image can highlight the changed areas between the two images, which may correspond to defects, movement or changes on the surface of the object.

[0115] The difference image may contain a large amount of noise and subtle variations that may not correspond to actual defects. To filter out this noise, thresholding can be applied to the difference image. This involves classifying the pixel values in the difference image into two categories: pixels above the threshold are considered defects, while pixels below the threshold are considered background or noise.

[0116] Because the upper / lower surface images and the reference upper / lower surface images may differ in position or angle when captured, they must be aligned to ensure spatial consistency. This can be achieved through feature matching, affine transformation, or other image registration algorithms.

[0117] In order to improve the effect of image alignment, the upper / lower surface image may be first denoised, and then the denoised upper / lower surface image and the reference upper / lower surface image may be registered.

[0118] Defect-related features, such as texture features, shape features, color features, etc., can be first extracted from the upper / lower surface images and the reference upper / lower surface images. Surface defect detection can be performed based on the extracted features using a defect detection model. Surface defect detection can also be performed based on the upper / lower surface images and the reference upper / lower surface images using a defect detection model. The defect detection model is a pre-trained machine learning model that can identify and locate defects on the surface of the copper foil. The defect-related features extracted from the upper / lower surface images and the reference upper / lower surface images, or the upper / lower surface images and the reference upper / lower surface images are directly used as input to the defect detection model, and the defect detection results (first upper surface defect detection results / second upper surface defect detection results) are output through the defect detection model. The surface defect detection results include all detected defect information, such as the location, size, shape, type, and other information of the defects, as well as one or more images or visual marks representing the defects. The defect information can be presented using numbers, images, or a combination of both.

[0119] After obtaining the first and second top surface defect detection results, the second top surface defect detection results can be used to correct errors or uncertainties in the first top surface defect detection results. For example, the defect locations, sizes, and types in the first and second top surface defect detection results can be matched, merged, or filtered to generate a final top surface defect detection result.

[0120] Similarly, after obtaining the first and second lower surface defect detection results, the second lower surface defect detection results can be used to correct errors or uncertainties in the first lower surface defect detection results. For example, the defect locations, sizes, and types in the first and second lower surface defect detection results can be matched, merged, or filtered to generate a final lower surface defect detection result.

[0121] It is understandable that before the differential image is input into the defect detection model, denoising, contrast enhancement, etc. can be performed to enhance the quality of the differential image and improve the accuracy of the detection results.

[0122] It is understandable that in order to further improve the accuracy of the detection results, the output of the defect detection model can be post-processed, such as removing isolated points, filling holes, etc.

[0123] It is understandable that the defect detection model used for defect detection on the lower surface image and the defect detection model used for defect detection based on the lower surface image can be the same model or different models, and this application does not impose any restrictions on this.

[0124] In the above optional implementation, direct defect detection on the upper / lower surface image may be affected by various factors, such as illumination changes, image noise, surface texture differences, etc., resulting in false alarms (misjudging non-defective areas as defects) or missed alarms (failure to detect real defects). The reference upper / lower surface image represents a defect-free or standard upper / lower surface of the copper foil. Through differential processing, the influence of the above factors on the upper / lower surface detection results can be eliminated or reduced, because the differential image mainly reflects the difference between the upper / lower surface image and the reference upper / lower surface image. Using the detection results of the first differential image to correct the detection results of the upper surface image and using the detection results of the second differential image to correct the detection results of the lower surface image can further reduce false alarms and missed alarms and improve the accuracy of detection. In addition, differential processing can highlight the areas where the upper / lower surface image is inconsistent with the reference upper / lower surface image, which are more likely to be defects. By focusing on these inconsistent areas, attention can be reduced on non-critical areas in the image, thereby reducing the interference of external factors on the detection results and enhancing the robustness of detection.

[0125] S17, generating a copper foil surface defect detection result according to the hidden defect detection result, the upper surface defect detection result, and the lower surface defect detection result.

[0126] The defects in the hidden defect detection results, upper surface defect detection results, and lower surface defect detection results can be assumed to be real. The information about defect characteristics (such as location, size, shape, etc.) in the hidden defect detection results, upper surface defect detection results, and lower surface defect detection results are combined to generate a more comprehensive defect description, which helps to more accurately determine the nature and severity of the defects. The result of the fusion of the three defect detection results is used as the final copper foil surface defect detection result.

[0127] In an optional embodiment, the method further comprises:

[0128] Determining the defect location according to the copper foil surface defect detection result;

[0129] The defect position is sent to a labeling machine, so that the labeling machine labels an area corresponding to the defect position on the copper foil to be tested.

[0130] After the system detects a defect, it records the location of the defect in the image and sends the defect location to the labeling machine.

[0131] After receiving the defect location, the labeling machine applies the label to the area corresponding to the defect location on the copper foil. The labeling machine usually has a high-precision positioning system and a reliable actuator to ensure that the label can be accurately attached to the defect location.

[0132] In some optional embodiments, before labeling, the system can select the appropriate label type (such as color, shape, size, etc.) according to the type and severity of the defect, and print or prepare it, thereby helping to quickly identify and handle different types of defects during the production process.

[0133] After labeling is completed, production personnel can quickly locate the defective copper foil area based on the label information and take corresponding treatment measures (such as rework, scrapping, etc.).

[0134] The above optional implementation method, by introducing precise marking of defect locations during the copper foil surface defect detection process, not only improves the automation level of the detection process, but also helps to quickly identify and handle defective copper foil in actual production.

[0135] In an optional embodiment, the copper foil surface defect detection results can be classified and counted, and the classification and statistics results can be obtained by Figure 2 The defect detection display interface shown is visually displayed. The defect detection display interface may include basic information of the copper foil, detection method, detection time, detected defect type, characteristics, location, priority and other information.

[0136] The system can count the number of defects on the upper and lower surfaces, as well as the total number of defects. It can also calculate the average size of defects and analyze their changing trends. It can also display a defect distribution map, helping you understand the distribution of various defects on the copper foil surface and their impact on the quality of the copper foil.

[0137] A set of standardized report templates can be pre-designed and used to generate copper foil quality inspection reports. The template includes basic report information (such as report number, inspection date, inspector, etc.), inspection results (such as the number and distribution of various defects), data analysis results (such as the average size of defects and change trends, etc.), and recommended quality control measures.

[0138] The above optional implementation method makes the detection results clearer and more intuitive by classifying and counting the copper foil surface defect detection results and generating a copper foil quality inspection report, so that enterprise managers and production personnel can have a more comprehensive understanding of the quality status of the copper foil and thus take more effective quality control measures.

[0139] The copper foil surface defect detection method provided by the present invention dynamically adjusts the real-time acquisition speeds of the first and second linear array CCD cameras based on the real-time movement speed of the copper foil to be tested and a preset baseline acquisition speed. This dynamic adjustment mechanism ensures that the cameras always capture images at the optimal frame rate, avoiding image blur or frame dropouts, thereby improving the acquisition quality of copper foil surface images and avoiding unnecessary image acquisition and storage, reducing the waste of computing resources and storage space. Two linear array CCD cameras are used to capture images of the upper and lower surfaces of the copper foil, respectively, ensuring temporal synchronization. This allows the inspection process to cover the entire surface of the copper foil, improving the comprehensiveness of the inspection. An image processing algorithm is used to obtain corresponding feature point pairs in the upper and lower surface images. These feature point pairs can accurately capture subtle changes in the upper and lower surfaces of the copper foil, thereby more accurately identifying potential defect areas. Surface defect detection is performed on the potential defect areas, the upper surface images, and the lower surface images, respectively, to obtain hidden defect detection results, upper surface defect detection results, and lower surface defect detection results. This multi-dimensional inspection method can more comprehensively detect various types of defects on the copper foil surface, improving the accuracy and reliability of the inspection. Finally, based on the hidden defect detection results, upper surface defect detection results, and lower surface defect detection results, the copper foil surface defect detection results are generated comprehensively. The comprehensive detection results can fully reflect the quality status of the copper foil surface.

[0140] Figure 3 This is a functional module diagram of the copper foil surface defect detection device provided in an embodiment of the present application.

[0141] In some embodiments, the copper foil surface defect detection device 30 may include a plurality of functional modules composed of program code segments. The program code of each program segment in the copper foil surface defect detection device 30 may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) The function of copper foil surface defect detection.

[0142] In this embodiment, the copper foil surface defect detection device 30 can be divided into multiple functional modules based on their functions. These functional modules may include a camera adjustment module 301, an image acquisition module 302, a feature acquisition module 303, a region identification module 304, a first detection module 305, a second detection module 306, a result generation module 307, and a copper foil labeling module 308. A module, as referred to herein, refers to a series of computer-readable instruction segments that can be executed by at least one processor and perform a fixed function, and is stored in a memory. The functions of each module in this embodiment will be described in detail in subsequent embodiments.

[0143] The camera adjustment module 301 is used to adjust the real-time acquisition speed of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and the preset reference acquisition speed;

[0144] The image acquisition module 302 is configured to acquire an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and acquire a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed;

[0145] The feature acquisition module 303 is used to acquire corresponding feature point pairs in the upper surface image and the lower surface image;

[0146] The region identification module 304 is configured to identify potential defect regions in the upper surface image and the lower surface image based on the corresponding feature point pairs;

[0147] The first detection module 305 is used to perform surface defect detection on the potential defect area to obtain a hidden defect detection result;

[0148] The second detection module 306 is configured to perform surface defect detection on the upper surface image to obtain an upper surface defect detection result, and to perform surface defect detection on the lower surface image to obtain a lower surface defect detection result;

[0149] The result generating module 307 is configured to generate a copper foil surface defect detection result based on the hidden defect detection result, the upper surface defect detection result, and the lower surface defect detection result.

[0150] The copper foil labeling module 308 is configured to determine a defect location based on the copper foil surface defect detection result; and send the defect location to a labeling machine so that the labeling machine labels an area corresponding to the defect location on the copper foil to be tested.

[0151] The copper foil surface defect detection device provided in the embodiments of the present application dynamically adjusts the real-time acquisition speeds of the first and second linear array CCD cameras based on the real-time movement speed of the copper foil to be tested and a preset baseline acquisition speed. This dynamic adjustment mechanism ensures that the cameras always capture images at the optimal frame rate, avoiding image blur or frame dropouts, thereby improving the acquisition quality of copper foil surface images and avoiding unnecessary image acquisition and storage, reducing the waste of computing resources and storage space. Using two linear array CCD cameras to capture images of the upper and lower surfaces of the copper foil, respectively, ensuring temporal synchronization, the detection process covers the entire surface of the copper foil, improving the comprehensiveness of the inspection. An image processing algorithm is used to obtain corresponding feature point pairs in the upper and lower surface images. These feature point pairs can accurately capture subtle changes in the upper and lower surfaces of the copper foil, thereby more accurately identifying potential defect areas. Surface defect detection is performed on the potential defect areas, the upper and lower surface images, and the images of the upper and lower surfaces, respectively, to obtain hidden defect detection results, upper surface defect detection results, and lower surface defect detection results. This multi-dimensional inspection method can more comprehensively detect various types of defects on the copper foil surface, improving the accuracy and reliability of the inspection. Finally, based on the hidden defect detection results, upper surface defect detection results, and lower surface defect detection results, the copper foil surface defect detection results are generated comprehensively. The comprehensive detection results can fully reflect the quality status of the copper foil surface.

[0152] It should be understood that the various variations and specific embodiments of the copper foil surface defect detection method provided in the above embodiments are also applicable to the copper foil surface defect detection device in this embodiment. Through the detailed description of the above copper foil surface defect detection method, those skilled in the art can clearly understand the implementation process of the copper foil surface defect detection device in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0153] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, all or part of the steps of the copper foil surface defect detection method are implemented.

[0154] See Figure 4 FIG. 4 is a schematic diagram of the structure of a computer device according to an embodiment of the present application. In a preferred embodiment of the present application, the computer device 4 includes a memory 401 , at least one processor 402 , and at least one communication bus 403 .

[0155] Those skilled in the art should understand that Figure 4 The structure of the computer device shown does not constitute a limitation of the embodiments of the present application. The computer device 4 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0156] In some embodiments, the computer device 4 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The computer device 4 may also include client devices, which include, but are not limited to, any electronic product capable of human-computer interaction with a client via a keyboard, mouse, remote control, touchpad, or voice-controlled device, such as a personal computer, tablet computer, smartphone, digital camera, etc.

[0157] It should be noted that the computer device 4 is only an example. Other existing or future electronic products that are suitable for this application should also be included in the scope of protection of this application and included here by reference.

[0158] In some embodiments, the memory 401 stores a computer program, and when the computer program is executed by the at least one processor 402, all or part of the steps in the copper foil surface defect detection method as described above are implemented. The memory 401 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data. Further, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.

[0159] In some embodiments, the at least one processor 402 is the control core (CrolUnit) of the computer device 4, which uses various interfaces and lines to connect the various components of the entire computer device 4, and executes various functions and processes data of the computer device 4 by running or executing the programs or modules stored in the memory 401, and calling the data stored in the memory 401. For example, when the at least one processor 402 executes the computer program stored in the memory, it implements all or part of the steps of the copper foil surface defect detection method described in the embodiment of the present application; or implements all or part of the functions of the copper foil surface defect detection device. The at least one processor 402 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips.

[0160] In some embodiments, the at least one communication bus 403 is configured to implement connection and communication between the memory 401 and the at least one processor 402, etc. Although not shown, the computer device 4 may also include a power supply (such as a battery) to power each component. Preferably, the power supply can be logically connected to the at least one processor 402 through a power management device, so that functions such as charging, discharging, and power consumption management are managed through the power management device. The power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 4 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0161] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is only a logical function division, and other division methods may be used in actual implementation.

[0163] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, and may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of this embodiment based on actual needs.

Claims

1. A method for detecting copper foil surface defects, characterized in that: The method comprises: Adjusting the real-time acquisition speed of the first and second linear array CCD cameras according to the real-time moving speed of the copper foil to be tested and the preset reference acquisition speed; Acquire an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and acquire a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed; Acquire corresponding feature point pairs in the upper surface image and the lower surface image; identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs; Performing surface defect detection on the potential defect area to obtain hidden defect detection results; Performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result; Generate a copper foil surface defect detection result according to the hidden defect detection result, the upper surface defect detection result, and the lower surface defect detection result; The identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs includes: Calculating the displacement vector between the corresponding feature point pairs; determining whether there is a displacement between the upper surface image and the lower surface image according to the displacement vector; When it is determined that there is displacement, the area corresponding to the displacement vector in the upper surface image and the lower surface image is identified as a potential defect area.

2. The copper foil surface defect detection method according to claim 1, characterized in that: The step of adjusting the real-time acquisition speeds of the first and second linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and the preset reference acquisition speed includes: Calculating a moving speed ratio according to the real-time moving speed of the copper foil to be tested and a preset reference moving speed; Determining the quality grade according to the model parameters of the copper foil to be tested; Obtaining the micro-texture change rate of the copper foil to be tested; The real-time acquisition speeds of the first group of linear array CCD cameras and the second group of linear array CCD cameras are adjusted according to the moving speed ratio, the quality level, the micro texture change rate and the preset reference acquisition speed.

3. The copper foil surface defect detection method according to claim 2, characterized in that: The acquiring corresponding feature point pairs in the upper surface image and the lower surface image comprises: Performing feature point detection on the upper surface image to obtain a first key feature point; Performing feature point detection on the lower surface image to obtain second key feature points; Feature matching is performed on the first key feature point and the second key feature point to obtain corresponding feature point pairs.

4. The copper foil surface defect detection method according to claim 1, characterized in that: The performing surface defect detection on the upper surface image to obtain an upper surface defect detection result, and performing surface defect detection on the lower surface image to obtain a lower surface defect detection result includes: performing a difference process on the upper surface image and a reference upper surface image to obtain a first difference image; Performing surface defect detection based on the upper surface image to obtain a first upper surface defect detection result; Performing surface defect detection based on the first differential image to obtain a second upper surface defect detection result; Correcting the first upper surface defect detection result according to the second upper surface defect detection result to obtain the upper surface defect detection result; performing a differential process on the lower surface image and a reference lower surface image to obtain a second differential image; Performing surface defect detection based on the lower surface image to obtain a first lower surface defect detection result; performing surface defect detection based on the second differential image to obtain a second lower surface defect detection result; The first lower surface defect detection result is corrected according to the second lower surface defect detection result to obtain the lower surface defect detection result.

5. The copper foil surface defect detection method according to claim 1, characterized in that: The method further comprises: Determining the defect location according to the copper foil surface defect detection result; The defect position is sent to a labeling machine, so that the labeling machine labels an area corresponding to the defect position on the copper foil to be tested.

6. The copper foil surface defect detection method according to claim 1, characterized in that: The first group of linear array CCD cameras is used to capture the upper surface of the copper foil to be tested to obtain the upper surface image under the lighting environment provided by the upper surface detection light source and the high-brightness backlight source; the second group of linear array CCD cameras is used to capture the lower surface of the copper foil to be tested to obtain the lower surface image under the lighting environment provided by the lower surface detection light source.

7. A copper foil surface defect detection device, characterized in that: The device comprises: A camera adjustment module is used to adjust the real-time acquisition speed of the first group of linear array CCD cameras and the second group of linear array CCD cameras according to the real-time moving speed of the copper foil to be measured and the preset reference acquisition speed; an image acquisition module, configured to acquire an upper surface image of the copper foil to be tested acquired by the first group of linear array CCD cameras at the real-time acquisition speed, and to acquire a lower surface image acquired by the second group of linear array CCD cameras at the real-time acquisition speed; A feature acquisition module, configured to acquire corresponding feature point pairs in the upper surface image and the lower surface image; an area recognition module, configured to recognize potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs; A first detection module is used to perform surface defect detection on the potential defect area to obtain a hidden defect detection result; a second detection module, configured to perform surface defect detection on the upper surface image to obtain an upper surface defect detection result, and to perform surface defect detection on the lower surface image to obtain a lower surface defect detection result; A result generating module, configured to generate a copper foil surface defect detection result based on the hidden defect detection result, the upper surface defect detection result, and the lower surface defect detection result; The identifying potential defect areas in the upper surface image and the lower surface image according to the corresponding feature point pairs includes: Calculating the displacement vector between the corresponding feature point pairs; determining whether there is a displacement between the upper surface image and the lower surface image according to the displacement vector; When it is determined that there is displacement, the area corresponding to the displacement vector in the upper surface image and the lower surface image is identified as a potential defect area.

8. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the copper foil surface defect detection method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the copper foil surface defect detection method according to any one of claims 1 to 6 are implemented.

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