A method and device for detecting visual defects of a thermal battery electrode sheet

Abstract

The present application belongs to the field of machine vision defect detection, and in particular to a thermocell electrode sheet vision defect detection method and a vision defect detection device. Product size information of a thermocell electrode sheet is obtained, and the field of view of a camera is configured. A thermocell electrode sheet to be detected is placed on a detection platform, front surface images, side surface images, back surface images, and inner hole images of the thermocell electrode sheet are captured by multiple direction detection cameras, and are processed to obtain the to-be-detected images of the front surface images, the side surface images, the back surface images, and the inner hole images. According to the to-be-detected images of the front surface images, the side surface images, the back surface images, and the inner hole images, a trained YOLO8 recognition model is used to describe and determine defects.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of machine vision defect detection, in particular to a thermal battery electrode sheet vision defect detection method and a vision defect detection device. BACKGROUND

[0002] Thermal battery is also called molten salt battery and thermal activated reserve battery. The electrolyte is non-conductive solid when stored, and the internal heating agent is ignited by an electric ignition head or a striker mechanism to make the electrolyte in the electrode sheet melt into an ionic conductor to activate the reserve battery when used.

[0003] Thermal battery is a high-temperature energy source with a working temperature of 350-500 DEG C. In addition to having positive and negative electrodes, the two electrodes are usually separated by an ionically conductive molten electrolyte. Before the electrolyte melts, the battery is emotional and can be stored for a long time. Once activated by ignition, it can discharge at high power, with a current of several amperes per square centimeter for several seconds, or it can discharge at low power for a long time, for 1 hour or more.

[0004] In the processing and production process of the thermal battery electrode sheet, low temperature, fire prevention and moisture prevention are required, and the process level and environment are very high, and the production difficulty is extremely great. In the production process, the defects of the thermal battery electrode sheet need to be detected, and the existing detection is all through manual visual inspection, which is low in detection efficiency, and after the electrode sheet is detected, manual classification and marking of the qualified and unqualified products are required. The whole detection process needs to consume manpower and has the risk of damaging the electrode sheet. Therefore, the application provides a vision defect detection method and device for the thermal battery electrode sheet. SUMMARY

[0005] The application aims to provide a thermal battery electrode sheet vision defect detection method and device, which has high measurement accuracy, fast response and can be effectively used for defect detection of the thermal battery electrode sheet.

[0006] The technical scheme adopted by the application to achieve the above-mentioned purpose is: a thermal battery electrode sheet vision defect detection method, comprising the following steps:

[0007] S1: obtaining product size information of the thermal battery electrode sheet and configuring a camera field of view;

[0008] S2: placing the thermal battery electrode sheet to be detected on a detection platform, and placing it directly below the front detection camera, taking a front image of the thermal battery electrode sheet by the front detection camera, and processing to obtain a to-be-detected image of the front image;

[0009] S3: rotating the thermal battery electrode sheet four times, and sequentially taking side images of the thermal battery electrode sheet and processing to obtain to-be-detected images of the side images;

[0010] S4: The electrode sheet to be inspected is moved to the top of the back detection camera, and the back image of the thermal battery electrode sheet is captured and processed to obtain the back image to be inspected;

[0011] S5: placing the electrode sheet to be tested in the testing tank, moving the testing tank to the bottom of the inner hole camera, capturing the inner hole image, and processing the image to be tested;

[0012] S6: Based on the images to be inspected, namely, the front image, the side image, the back image, and the inner hole image, the defects are described and determined using the trained recognition model.

[0013] Execute step S2 and step S4 to obtain the image to be detected of the front image and the image to be detected of the back image, specifically:

[0014] S2-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI);

[0015] S2-2: Perform Hough circle detection on the binary image to obtain the center and radius information of all circles that meet the conditions;

[0016] S2-3: Cut the region of interest (ROI) image based on the circle center position and radius information to obtain the image between the inner hole and the outer contour of the thermal battery;

[0017] S2-4: Cut the image between the inner hole and the outer contour of the thermal battery by high threshold and low threshold to obtain the defect area image.

[0018] In step 2-2, the center and radius information of all circles that meet the conditions are obtained, specifically:

[0019] (1) Detect the outer edge of the thermal battery electrode image and find the positions of all circle centers;

[0020] (2) Calculate the distance from the outer edge circle center to other pixel points to obtain the radius;

[0021] (3) Detect the inner holes of the thermal battery electrode image and find the positions of all circle centers;

[0022] (4) Calculate the distance from the inner hole center to other pixel points to obtain the radius;

[0023] (5) Based on the product size information of the thermoelectric battery electrode sheet obtained in step S1, screen whether there is an inner hole circle and an outer contour circle that meet the size requirements.

[0024] The step S2-4 is specifically as follows:

[0025] a. Detect the pixels in the image that are greater than the high threshold and obtain the highlighted area;

[0026] b. Detect the pixels in the image that are smaller than the low threshold and obtain the low-brightness area;

[0027] c. Filter out interference in the image through two erosion operations and two dilation operations;

[0028] d. Merge the highlight area and the low-brightness area to obtain the image to be detected.

[0029] The step S3 is specifically as follows:

[0030] S3-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the center of the image as the reference, 2.4 times the radius as the length, and 0.3 of the radius as the width to determine the region of interest (ROI);

[0031] S3-2: Filter out interference in the image through one erosion operation and one dilation operation;

[0032] S3-3: Cut the highlight area in the image by a high threshold to obtain the image to be detected;

[0033] S3-4: Rotate the electrode sheet and repeatedly perform steps S3-1 to S3-3, that is, take four pictures in total to cover the entire side of the electrode sheet.

[0034] The step S5 is specifically as follows:

[0035] S5-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI);

[0036] S5-2: Filter out interference in the image through one erosion operation and one dilation operation;

[0037] S5-3: Cut out the highlight area in the image through a high threshold to obtain the image to be detected.

[0038] The trained recognition model includes: a detection model and a classification model;

[0039] The trained recognition model makes a determination based on any one of the support vector machine and neural network results.

[0040] The trained recognition model is used to describe and determine defects, specifically:

[0041] Wherein, the recognition model is the Yolov8 deep learning model;

[0042] S6-1: by inputting the image to be detected into the Yolov8 deep learning model in turn, and then performing forward propagation calculation, the defect information existing in the image is obtained, that is, the defect size and the confidence of each defect;

[0043] S6-2: compare the confidence with the artificially set fixed confidence threshold, segment the defect information, if the defect information greater than the confidence threshold appears, it is determined that there is a defect; then compare with the set defect size threshold, if greater than the defect size threshold, it is determined that there is an unacceptable defect, and record the defect type, defect size and defect number; if less than the defect size threshold, it is determined that it is an acceptable defect, and record the qualified information;

[0044] The defect information less than the confidence threshold is determined as an acceptable defect or no defect, and the qualified information is recorded.

[0045] The lighting mode of the photographed image is any one of ring dome lighting and side arc lighting.

[0046] A hot battery electrode sheet visual defect detection device, comprising: a frame body, an industrial camera, a camera light source, a light source support, a detection groove, a mechanical hand, a PC, a light source controller, a PLC, a rotating workbench, a feeding station and a transfer module;

[0047] The industrial camera comprises: a front industrial camera, a side industrial camera, a back industrial camera and an inner hole industrial camera;

[0048] The camera light source comprises: a front camera light source, a side camera light source, a back camera light source and an inner hole camera light source;

[0049] The light source support comprises: a front camera light source support, a side camera light source support, a back camera light source support and an inner hole camera light source support;

[0050] The transfer module is arranged on the frame body table top; the mechanical hand is arranged on the transfer module and connected with the PLC to suck the hot battery electrode sheet and move above the frame body table top;

[0051] The feeding station is arranged on the left side of the frame body table top and below the leftmost side of the transfer module, used for the mechanical hand to suck the hot battery electrode sheet transmitted from the upper station;

[0052] The front camera light source support is arranged on the frame body table top and on the right side of the feeding station; the front industrial camera is arranged on the top of the front camera light source support and the field of view direction covers the front of the hot battery electrode sheet; the front camera light source is arranged around the lens outer circle of the front industrial camera; the rotating workbench is arranged on the frame body table top and directly below the front industrial camera;

[0053] The side camera light source bracket is arranged on the frame table and to the right of the front camera light source bracket; the side industrial camera is arranged on the side camera light source bracket, and the field of view direction covers the side of the thermal battery electrode sheet; the side camera light source is arranged on the right side of the rotating workbench; the side camera light source bracket carries the side industrial camera and moves back and forth to capture images of different types of electrode sheets;

[0054] The rear camera light source bracket is arranged on the frame table and below the right side of the transfer module; the rear camera light source is arranged around the outer ring of the lens of the rear industrial camera, and the field of view of the rear industrial camera covers the back of the thermal battery electrode sheet;

[0055] The inner hole camera light source bracket is arranged on the frame table and on the right side bracket of the transfer module;

[0056] The inner hole industrial camera is arranged on the inner hole camera light source bracket, and the field of view direction covers the inner hole of the electrode sheet; the inner hole camera light source is arranged around the outer circle of the lens of the inner hole industrial camera;

[0057] The inspection tank is set on the frame table and connected to the PLC. It is used to carry the electrode sheet and move it to the bottom of the inner hole industrial camera. After the inspection is completed, the electrode sheet is sucked away by the lower station.

[0058] The PC is connected to the light source controller, PLC and industrial camera respectively, and is used to process the electrode sheet images sent back by the industrial camera. It also controls the camera light source for lighting through the light source controller and can interact with the PLC for information.

[0059] The present invention has the following beneficial effects and advantages:

[0060] 1. The device of the present invention determines the key feature information of the thermal battery electrode sheet in the image through the recognition model, has a fast response and high accuracy, and can be effectively used for defect detection of the thermal battery electrode sheet.

[0061] 2. During the production of thermal battery electrodes, low temperature, fire protection, and moisture resistance are required. Therefore, the entire production process is carried out in a low-temperature drying room. This device has application significance in solving the problem of manual inspection difficulties caused by production in a low-temperature drying room.

[0062] 3. Through judgment based on deep learning recognition model, the defect information of thermal batteries can be effectively extracted, which makes up for the lack of accuracy brought by traditional methods and plays an important role when traditional methods cannot determine defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic structural diagram of an embodiment of a visual defect detection device according to the present invention;

[0064] Figure 2This is a schematic diagram of the main detection surface of the thermal battery electrode;

[0065] Figure 3 This is a schematic diagram of the side detection surface of the thermal battery electrode;

[0066] Figure 4 Schematic diagram of a process for visual defect detection of thermal battery electrode sheets according to the present invention;

[0067] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention;

[0068] Among them, 1 is the front industrial camera, 2 is the front camera light source, 3 is the side industrial camera, 4 is the side camera light source, 5 is the back industrial camera, 6 is the back camera light source, 7 is the inner hole industrial camera, 8 is the inner hole camera light source, 9 is the inspection slot, 10 is the robot, 11 is the PC, 12 is the light source controller, 13 is the PLC, 14 is the rotary workbench, 15 is the loading station, 16 is the transfer module, 17 is the front camera light source bracket, 18 is the side camera light source bracket, 19 is the back camera light source bracket, and 20 is the inner hole camera light source bracket. DETAILED DESCRIPTION

[0069] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0070] like Figure 4 FIG. 1 is a flow chart of a method for detecting visual defects of a thermal battery electrode sheet according to the present invention. The method for detecting visual defects of a thermal battery electrode sheet according to the present invention comprises the following steps:

[0071] S1: Obtain product size information of the thermal battery electrode sheet and configure the camera field of view;

[0072] S2: Pick up the electrode sheet to be tested and place it directly below the detection camera on the front of the detection platform to capture the front image of the electrode sheet;

[0073] S2-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI);

[0074] S2-2: Perform Hough circle detection on the binary image to obtain the center and radius information of all circles that meet the conditions.

[0075] The method of obtaining the center and radius information of all circles that meet the conditions is as follows:

[0076] a. Detect the outer edge of the thermal battery electrode image and find the positions of all circle centers;

[0077] b. Based on the outer edge circle center, the distance to other pixel points is calculated to obtain the radius size;

[0078] c. The inner hole of the thermal battery electrode sheet image is detected to find all the circle center positions;

[0079] d. Based on the inner hole circle center, the distance to other pixel points is calculated to obtain the radius size;

[0080] e. According to the product size information of the thermal battery electrode sheet obtained in step S1, it is determined whether there is an inner hole circle and an outer contour circle that meet the size.

[0081] S2-3: According to the circle center position and the radius information, the region of interest (ROI) image is cut to obtain the image between the inner hole and the outer contour of the thermal battery.

[0082] S2-4: The image between the inner hole and the outer contour of the thermal battery is cut by high threshold and low threshold to obtain the defect region image, specifically:

[0083] a. Detect the pixel points in the image that are greater than the high threshold to obtain the highlight area;

[0084] b. Detect the pixel points in the image that are greater than the low threshold to obtain the low-light area;

[0085] c. Through two erosion operations and two inflation operations, the interference in the image is filtered out;

[0086] d. Merge the highlight area and the low-light area to obtain the image to be detected.

[0087] S3: Rotate the thermal battery electrode sheet four times and take side images in turn;

[0088] S3-1: According to the radius information, the position of the region of interest (ROI) of the thermal battery electrode sheet is determined, that is, the center of the image is taken as the reference, the radius is 2.4 times the length, and the radius is 0.3 times the width. Cut the original image to determine the region of interest (ROI);

[0089] S3-2: Through one erosion operation and one inflation operation, the interference in the image is filtered out;

[0090] S3-3: The highlight area in the image is cut by high threshold to obtain the image to be detected;

[0091] S3-4: Rotate the electrode sheet and repeatedly perform the operations of S3-1, S3-2, and S3-3. A total of four pictures are taken to cover the entire electrode sheet side.

[0092] S4: The electrode sheet to be detected is moved to the front of the back detection camera and the back image of the electrode sheet is taken;

[0093] S4-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI);

[0094] S4-2: Perform Hough circle detection on the binary image to obtain the center and radius information of all circles that meet the conditions;

[0095] The method of obtaining the center and radius information of all circles that meet the conditions is as follows:

[0096] a. Detect the outer edge of the thermal battery electrode image and find the positions of all circle centers;

[0097] b. Calculate the distance from the outer edge center to other pixels to obtain the radius;

[0098] c. Detect the inner holes of the thermal battery electrode image and find the positions of all circle centers;

[0099] d. Calculate the distance from the center of the inner hole to other pixels to obtain the radius;

[0100] e. Based on the product size information of the thermoelectric battery electrode sheet obtained in step S1, screen whether there is an inner hole circle and an outer contour circle that meet the size.

[0101] S4-3: Cut the region of interest (ROI) image based on the circle center position and radius information to obtain the image between the inner hole and the outer contour of the thermal battery;

[0102] S4-4: Cut the image between the inner hole and the outer contour of the thermal battery by high threshold and low threshold, and obtain the defect area image, specifically:

[0103] a. Detect the pixels in the image that are greater than the high threshold and obtain the highlighted area;

[0104] b. Detect the pixels in the image that are larger than the low threshold and obtain the low-brightness area;

[0105] c. Filter out interference in the image through two erosion operations and two dilation operations;

[0106] d. Merge the highlight area and the low-brightness area to obtain the image to be detected.

[0107] S5: Place the electrode sheet to be tested in the test tank, move the test tank to the bottom of the inner hole camera, and take an image of the inner hole;

[0108] S5-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI);

[0109] S5-2: Filter out interference in the image through one erosion operation and one dilation operation;

[0110] S5-3: Cut out the highlight area in the image through a high threshold to obtain the image to be detected.

[0111] S6: Use the trained recognition model to describe and determine the defects. Figure 2 Figure 2 shows the main inspection surface of the thermoelectric battery electrode sheet of the present invention. In this embodiment, the thermoelectric battery electrode sheet is a circular multi-layer sheet, which, from top to bottom, comprises a graphite sheet, positive electrode powder, separator powder, negative electrode powder, and graphite sheet. Detected defects include dents, cracks, dirt, interconnection between positive and negative electrode powders, and separator powder breaks.

[0112] Both the detection and classification models are based on the Yolov8 model. By inputting the feature image into the Yolov8 deep learning model, a forward propagation calculation is performed to obtain information about defects in the image. A fixed threshold is used to segment the defect information and determine the degree of defects in the thermal battery electrode.

[0113] The above steps are specifically divided into the following steps to determine the degree of defects in the thermal battery electrode sheet, specifically:

[0114] S6-1: By inputting the images to be inspected into the Yolov8 deep learning model in sequence and then performing forward propagation calculations, the defect information in the image is obtained, namely: the defect size and the confidence level of each defect;

[0115] S6-2: Compare the confidence level with a manually set fixed confidence threshold, segment the defect information, and if defect information is greater than the confidence threshold, determine that a defect exists. Then compare it with the set defect size threshold. If it is greater than the defect size threshold, determine that an unacceptable defect exists, and record the defect type, defect size, and number of defects. If it is less than the defect size threshold, determine that the defect is acceptable, and record the qualified information.

[0116] Defect information that is less than the confidence threshold is determined to be an acceptable defect or no defect, and the qualified information is recorded at the same time.

[0117] like Figure 3 As shown, it is a schematic diagram of the side detection surface of the thermal battery electrode sheet of the present invention. Finally, the defects detected include: positive and negative electrode powder connection, isolation powder breakage, etc.

[0118] like Figure 1FIG. 1 is a schematic structural diagram of an embodiment of a visual defect detection device of the present invention. The present invention is a thermal battery electrode sheet visual defect detection device, characterized in that it includes: a frame, an industrial camera, a camera light source, a light source bracket, a detection tank (9), a manipulator (10), a PC (11), a light source controller (12), a PLC (13), a rotary table (14), a loading station (15) and a transfer module (16);

[0119] The industrial camera comprises: a front industrial camera (1), a side industrial camera (3), a back industrial camera (5) and an inner hole industrial camera (7);

[0120] The camera light source comprises: a front camera light source (2), a side camera light source (4), a back camera light source (6) and an inner hole camera light source (8);

[0121] The light source bracket comprises: a front camera light source bracket (17), a side camera light source bracket (18), a back camera light source bracket (19) and an inner hole camera light source bracket (20);

[0122] The transfer module (16) is arranged on the frame table; the manipulator (10) is arranged on the transfer module (16) and is connected to the PLC (13) to absorb the thermal battery electrode sheet and move it above the frame table;

[0123] The loading station (15) is located on the left side of the frame table and is located below the leftmost side of the transfer module (16), and is used for the manipulator (10) to absorb the hot battery electrode sheet transferred from the upper station;

[0124] The front camera light source bracket (17) is arranged on the frame table and on the right side of the loading station (15); the front industrial camera (1) is arranged on the top of the front camera light source bracket (17), and the field of view direction covers the front of the thermal battery electrode sheet; the front camera light source (2) is arranged around the outer ring of the lens of the front industrial camera (1); the rotating workbench (14) is arranged on the frame table and directly below the front industrial camera (1);

[0125] The side camera light source bracket (18) is arranged on the frame table and on the right side of the front camera light source bracket (17); the side industrial camera (3) is arranged on the side camera light source bracket (18), and the field of view direction covers the side of the thermal battery electrode sheet; the side camera light source (4) is arranged on the right side of the rotating workbench (14); the side camera light source bracket (18) carries the side industrial camera (3) and moves forward and backward to capture images of electrode sheets of different models;

[0126] The back camera light source support (19) is arranged on the table top of the frame body and below the right side of the transfer module (16); the back camera light source (6) is arranged around the lens outer ring of the back industrial camera (5), and the field of view direction of the back industrial camera (5) covers the back of the thermal battery electrode sheet;

[0127] The inner hole camera light source support (20) is arranged on the table top of the frame body and on the right side support of the transfer module (16);

[0128] The inner hole industrial camera (7) is arranged on the inner hole camera light source support (20), and the field of view direction covers the inner hole of the electrode sheet; the inner hole camera light source (8) is arranged around the lens outer ring of the inner hole industrial camera (7);

[0129] The detection groove (9) is arranged on the table top of the frame body and connected with the PLC (13), for carrying the electrode sheet to move below the inner hole industrial camera (7), and after detection is completed, the electrode sheet is sucked away by the lower station;

[0130] The PC (11) is connected with the light source controller (12), the PLC (13) and the industrial camera, for processing the electrode sheet image returned by the industrial camera, and also controls the camera light source to illuminate through the light source controller (12), and can interact with the PLC (13).

[0131] Embodiment 1:

[0132] In the specific implementation process, the thermal battery electrode sheet can be sucked by the mechanical hand from the magazine and placed directly below the detection equipment industrial camera, the front image is shot, and after completion, the electrode sheet is rotated to shoot four side images. Then the electrode sheet to be detected is sucked and moved to the upper side of the back detection camera, and the back image of the electrode sheet is shot. Continue to suck the electrode sheet to be detected and place it in the detection groove, move the detection groove to the lower side of the inner hole camera, and shoot the inner hole image. Finally, a trained recognition model is used to describe and judge the defects.

[0133] In addition, in order to ensure that the defect image is clear and easy to identify, the quality of the image needs to be ensured. In order to obtain high-quality images, for thermal battery electrode sheet detection, the front, bottom and inner hole are ring-shaped dome lighting, and the side is arc-shaped lighting.

[0134] As Figure 5 shown is a structure schematic diagram of a computer device provided by the embodiment of the application, such as a desktop computer, a notebook computer, an embedded mainboard, an Android all-in-one machine and the like. The computer device of the embodiment at least includes but is not limited to a processor and a memory connected through a bus. In the embodiment, the processor is used to run a program stored in the memory, so as to realize the thermal battery electrode sheet visual defect detection method in the embodiment.

Claims

1. A method for visual defect detection of thermal battery electrode sheets, characterized in that: The following steps are involved: S1: Obtain product size information of the thermal battery electrode sheet and configure the camera field of view; S2: The thermal battery electrode sheet to be inspected is placed on the inspection platform and directly below the front inspection camera. The front inspection camera captures the front image of the thermal battery electrode sheet and processes the image to be inspected. S3: rotating the thermal battery electrode sheet four times, and sequentially photographing side images of the thermal battery electrode sheet, and processing the images to be detected; S4: The electrode sheet to be inspected is moved to the top of the back detection camera, and the back image of the thermal battery electrode sheet is captured and processed to obtain the back image to be inspected; S5: placing the electrode sheet to be tested in the testing tank, moving the testing tank to the bottom of the inner hole camera, capturing the inner hole image, and processing the image to be tested; S6: Based on the images to be inspected, namely, the front image, the side image, the back image, and the inner hole image, the trained recognition model is used to describe and determine the defects; Wherein, executing step S2 and step S4 to obtain the image to be detected of the front image and the image to be detected of the back image is specifically as follows: S2-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI); S2-2: Perform Hough circle detection on the binary image to obtain the center and radius information of all circles that meet the conditions; S2-3: Cut the region of interest (ROI) image based on the circle center position and radius information to obtain the image between the inner hole and the outer contour of the thermal battery; S2-4: Cut the image between the inner hole and the outer contour of the thermal battery by high threshold and low threshold to obtain the defect area image; Step S2-4 is specifically as follows: a. Detect the pixels in the image that are greater than the high threshold and obtain the highlighted area; b. Detect the pixels in the image that are smaller than the low threshold and obtain the low-brightness area; c. Filter out interference in the image through two erosion operations and two dilation operations; d. Merge the highlight area and the low-brightness area to obtain the image to be detected.

2. The method for visual defect detection of thermal battery electrode sheets according to claim 1, characterized in that: In step 2-2, the center and radius information of all circles that meet the conditions are obtained, specifically: (1) Detect the outer edge of the thermal battery electrode image and find the positions of all circle centers; (2) Calculate the distance from the center of the outer edge circle to other pixels to obtain the radius; (3) Detect the inner holes of the thermal battery electrode image and find the positions of all circle centers; (4) Calculate the distance from the center of the inner hole to other pixels to obtain the radius; (5) Based on the product size information of the thermoelectric battery electrode sheet obtained in step S1, screen whether there is an inner hole circle and an outer contour circle that meet the size requirements.

3. The method for visual defect detection of thermal battery electrode sheets according to claim 1, characterized in that: The step S3 is specifically as follows: S3-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the center of the image as the reference, 2.4 times the radius as the length, and 0.3 of the radius as the width to determine the region of interest (ROI); S3-2: Filter out interference in the image through one erosion operation and one dilation operation; S3-3: Cut the highlight area in the image by a high threshold to obtain the image to be detected; S3-4: Rotate the electrode sheet and repeatedly perform steps S3-1 to S3-3, that is, take four pictures in total to cover the entire side of the electrode sheet.

4. The method for visual defect detection of thermal battery electrode sheets according to claim 1, characterized in that: The step S5 is specifically as follows: S5-1: Determine the location of the region of interest (ROI) of the thermoelectric battery electrode according to the radius information, that is, cut the original image with the image center as the base radius and 1.2 times as much as the radius to determine the region of interest (ROI); S5-2: Filter out interference in the image through one erosion operation and one dilation operation; S5-3: Cut out the highlight area in the image through a high threshold to obtain the image to be detected.

5. The method for visual defect detection of thermal battery electrode sheets according to claim 1, characterized in that: The trained recognition model includes: a detection model and a classification model; The trained recognition model is judged based on any one of the support vector machine and neural network results.

6. The method for visual defect detection of thermal battery electrode sheets according to claim 4, characterized in that: The trained recognition model is used to describe and determine defects, specifically: Wherein, the recognition model is the Yolov8 deep learning model; S6-1: By inputting the images to be inspected into the Yolov8 deep learning model in sequence and then performing forward propagation calculations, the defect information in the image is obtained, namely: the defect size and the confidence level of each defect; S6-2: Compare the confidence level with a manually set fixed confidence threshold, segment the defect information, and if defect information is greater than the confidence threshold, determine that a defect exists. Then compare it with the set defect size threshold. If it is greater than the defect size threshold, determine that an unacceptable defect exists, and record the defect type, defect size, and number of defects. If it is less than the defect size threshold, determine that the defect is acceptable, and record the qualified information. Defect information that is less than the confidence threshold is determined to be an acceptable defect or no defect, and the qualified information is recorded at the same time.

7. The method for visual defect detection of thermal battery electrode sheets according to claim 1, characterized in that: The lighting method of the photographed image is any one of annular dome lighting and side arc lighting.

8. The detection device for a thermal battery electrode sheet visual defect detection method according to any one of claims 1 to 7, characterized in that: include: Frame, industrial camera, camera light source, light source bracket, detection tank (9), robot (10), PC (11), light source controller (12), PLC (13), rotary table (14), loading station (15) and transfer module (16); The industrial camera comprises: a front industrial camera (1), a side industrial camera (3), a rear industrial camera (5) and an inner hole industrial camera (7); The camera light source comprises: a front camera light source (2), a side camera light source (4), a back camera light source (6) and an inner hole camera light source (8); The light source bracket comprises: a front camera light source bracket (17), a side camera light source bracket (18), a back camera light source bracket (19) and an inner hole camera light source bracket (20); The transfer module (16) is arranged on the frame table; the manipulator (10) is arranged on the transfer module (16) and is connected to the PLC (13) to absorb the thermal battery electrode sheet and move it above the frame table; The loading station (15) is located on the left side of the frame table and is located below the leftmost side of the transfer module (16), and is used for the manipulator (10) to absorb the hot battery electrode sheet transferred from the upper station; The front camera light source bracket (17) is arranged on the frame table and on the right side of the loading station (15); the front industrial camera (1) is arranged on the top of the front camera light source bracket (17), and the field of view direction covers the front of the thermal battery electrode sheet; the front camera light source (2) is arranged around the outer ring of the lens of the front industrial camera (1); the rotating workbench (14) is arranged on the frame table and directly below the front industrial camera (1); The side camera light source bracket (18) is arranged on the frame table and on the right side of the front camera light source bracket (17); the side industrial camera (3) is arranged on the side camera light source bracket (18), and the field of view direction covers the side of the thermal battery electrode sheet; the side camera light source (4) is arranged on the right side of the rotating workbench (14); the side camera light source bracket (18) carries the side industrial camera (3) and moves forward and backward to capture images of electrode sheets of different models; The back camera light source bracket (19) is arranged on the frame table and below the right side of the transfer module (16); the back camera light source (6) is arranged around the outer ring of the lens of the back industrial camera (5), and the field of view direction of the back industrial camera (5) covers the back of the thermal battery electrode sheet; The inner hole camera light source bracket (20) is arranged on the frame table and on the right side bracket of the transfer module (16); The inner hole industrial camera (7) is arranged on the inner hole camera light source bracket (20), and the field of view direction covers the inner hole of the electrode sheet; the inner hole camera light source (8) is arranged around the outer ring of the lens of the inner hole industrial camera (7); The detection tank (9) is arranged on the frame table and is connected to the PLC (13), and is used to carry the electrode sheet and move it to the bottom of the inner hole industrial camera (7). After the detection is completed, the electrode sheet is sucked away by the lower station; The PC (11) is connected to the light source controller (12), the PLC (13) and the industrial camera respectively, and is used to process the electrode sheet image sent back by the industrial camera, and also controls the camera light source for lighting through the light source controller (12), and can exchange information with the PLC (13).

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