Method, device and medium for detecting encapsulation of cylindrical battery cell end face

Abstract

The application discloses a method and device for detecting the encapsulation condition of the end face of a cylindrical battery cell, equipment and a medium, and relates to the technical field of industrial vision detection. The method comprises the following steps: obtaining a target end face encapsulation image of a cylindrical battery cell to be detected; performing insulation sheet detection processing on the target end face encapsulation image to obtain an insulation sheet detection result; when the insulation sheet detection result is that the insulation sheet exists, performing shielding area detection processing on the target end face encapsulation image to obtain a target fan-shaped shielding area; performing encapsulation area detection processing on the target end face encapsulation image to obtain an initial encapsulation area; subtracting the target fan-shaped shielding area from the initial encapsulation area to obtain an encapsulation effective detection area; performing radial scanning detection judgment processing on the encapsulation effective detection area to obtain a plurality of target encapsulation detection areas; and performing continuity defect detection processing on the plurality of target encapsulation detection areas to obtain an encapsulation defect detection result. The effective coverage rate and reliability of the encapsulation condition detection of the end face of the cylindrical battery cell can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial vision detection, and in particular to a method and device for detecting the rubber coating condition of the end face of a cylindrical battery cell, as well as an equipment and a medium. BACKGROUND

[0002] New energy batteries are increasingly widely used in life and industry. For example, new energy vehicles equipped with batteries have been widely used, and batteries are also increasingly used in the field of energy storage and the like. In the production process of cylindrical battery cells, after the cylindrical battery cells are wound and before they are put into the shell, in order to avoid short circuit of the positive and negative poles of the cylindrical battery cells and to ensure that the cylindrical battery cells can be smoothly put into the shell in subsequent processes, the positive tab of the cylindrical battery cell needs to be coated with rubber. After the tab is coated with rubber, the cylindrical battery cell usually needs to be detected for rubber coating to identify defects such as missing coating, wrinkling, lifting and damage of the tab.

[0003] However, in the related art, the rubber coating detection scheme for the cylindrical battery cell has the defects of insufficient interference area suppression capability and single width threshold comparison for defect determination, resulting in low detection reliability and affecting the quality of the final product. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a method and device for detecting the rubber coating condition of the end face of a cylindrical battery cell, which can improve the effective coverage rate and reliability of the rubber coating condition detection of the end face of the cylindrical battery cell.

[0005] In a first aspect, the embodiments of the present application provide a method for detecting the rubber coating condition of the end face of a cylindrical battery cell, comprising:

[0006] obtaining a target end face rubber coating image of a cylindrical battery cell to be detected;

[0007] performing insulation sheet detection processing on the target end face rubber coating image to obtain an insulation sheet detection result;

[0008] when the insulation sheet detection result is that there is an insulation sheet, performing shielding area detection processing on the target end face rubber coating image to obtain a target fan-shaped shielding area; the target fan-shaped shielding area includes an extension handle of the end face of the cylindrical battery cell;

[0009] performing rubber coating area detection processing on the target end face rubber coating image to obtain an initial rubber coating area;

[0010] subtracting the target fan-shaped shielding area from the initial rubber coating area to obtain a rubber coating effective detection area;

[0011] performing radial scanning detection and determination processing on the rubber coating effective detection area to obtain a plurality of target rubber coating detection areas;

[0012] The continuity defect detection processing is performed on the plurality of target encapsulation detection areas to obtain encapsulation defect detection results.

[0013] In a second aspect, an encapsulation condition detection device for an end face of a cylindrical battery cell is provided, including at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the encapsulation condition detection method for the end face of the cylindrical battery cell according to any one of the embodiments of the first aspect.

[0014] In a third aspect, an electronic device is provided, including the encapsulation condition detection device for the end face of the cylindrical battery cell according to the embodiments of the second aspect.

[0015] In a fourth aspect, a computer readable storage medium is provided, which stores computer executable instructions for causing a computer to perform the encapsulation condition detection method for the end face of the cylindrical battery cell according to any one of the embodiments of the first aspect.

[0016] The embodiment of the application comprises the following steps: in the process of detecting the encapsulation condition of the end face of the cylindrical battery cell, firstly, an encapsulation image of the target end face of the cylindrical battery cell to be detected is acquired; secondly, insulation sheet detection processing is performed on the encapsulation image of the target end face to obtain an insulation sheet detection result; then, when the insulation sheet detection result is that the insulation sheet exists, shielding area detection processing is performed on the encapsulation image of the target end face to obtain a target fan-shaped shielding area; the target fan-shaped shielding area includes an extension handle of the end face of the cylindrical battery cell; the target fan-shaped shielding area is an area that will interfere with the encapsulation condition detection, and the target fan-shaped shielding area lays a foundation for subsequent determination of a target encapsulation detection area, so as to improve the effective coverage rate of the encapsulation detection; then, encapsulation area detection processing is performed on the encapsulation image of the target end face to obtain an initial encapsulation area; then, the initial encapsulation area is subtracted by the target fan-shaped shielding area to obtain an encapsulation effective detection area; the target fan-shaped shielding area that will interfere with the encapsulation condition detection is removed from the initial encapsulation area to obtain the encapsulation effective detection area, so as to improve the effective coverage rate of the encapsulation detection; then, radial scanning detection judgment processing is performed on the encapsulation effective detection area to obtain a plurality of target encapsulation detection areas; finally, continuity defect detection processing is performed on the plurality of target encapsulation detection areas to obtain an encapsulation defect detection result; the target encapsulation detection area can be used to quickly and reliably detect the encapsulation condition of the end face of the cylindrical battery cell. After the invalid target fan-shaped shielding area is removed, the encapsulation condition of the end face of the cylindrical battery cell is quickly and reliably detected based on the determined encapsulation effective detection area, and the effective coverage rate and reliability of the encapsulation condition detection are improved. That is, the embodiment of the application can improve the effective coverage rate and reliability of the encapsulation condition detection of the end face of the cylindrical battery cell.

[0017] Other features and advantages of the present application will be set forth in the following description, and in part will be apparent from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims thereof. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a flowchart of a method for detecting the encapsulation condition of the end face of a cylindrical battery cell provided by an embodiment of the application;

[0019] Figure 2 is a schematic diagram of the end face of a cylindrical battery cell provided by an embodiment of the application;

[0020] Figure 3 is a schematic diagram of an algorithm flow for generating a target fan-shaped shielding area provided by an embodiment of the application;

[0021] Figure 4 is a schematic diagram of the principle of radial scanning provided by an embodiment of the application;

[0022] Figure 5 is a whole process schematic diagram of a method for detecting the encapsulation condition of the end face of a cylindrical battery cell provided by an embodiment of the present application;

[0023] Figure 6 is a hardware structure schematic diagram of a device for detecting the encapsulation condition of the end face of a cylindrical battery cell provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments.

[0025] It should be noted that although a logical sequence is shown in the flowchart in the description of the present application, in some cases, the steps shown or described in the flowchart can be performed in an order different from that in the flowchart. In the description of the present application, the meaning of “one or more” is one or more, and the meaning of “multiple” is two or more. The description of “first”, “second” is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0027] First, some terms involved in the present application are explained:

[0028] Template matching: Template matching is a target detection method based on image similarity measurement, which finds the best matching position by sliding a predefined template image on the detection image pixel by pixel.

[0029] Binaryzation: Binaryzation is an image processing technique that converts grayscale or color images into images containing only two pixel values (usually 0 and 255, representing black and white).

[0030] Connected region: A region in an image where the pixel values are the same and adjacent.

[0031] Area filtering: Area filtering is a region screening technique in morphological image processing, which removes noise or invalid regions based on the area characteristics of connected domains.

[0032] Threshold segmentation: A technique that divides an image into foreground and background by comparing pixel grayscale values with a threshold value.

[0033] Color image channel separation: decompose the input RGB three-channel color image into a red component image, a green component image, and a blue component image.

[0034] Color space conversion: convert the RGB color space to the HSV color space to obtain a hue component image, a saturation component image, and a lightness component image.

[0035] Image / channel difference: Image / channel difference refers to generating a new difference image by calculating the difference between two images pixel by pixel.

[0036] ROI: Full name is Region of Interest, which refers to a box on a feature map. In machine vision and image processing, the region of interest is outlined in a box, circle, ellipse, irregular polygon, etc. from the image to be processed, which is called the region of interest.

[0037] Structural element: Create a circular structural element S(r) for morphological operation.

[0038] Gray open operation: erosion followed by dilation operation.

[0039] Gray dilation: maximum filtering of the image with the structural element S.

[0040] Minimum circumscribed circle: the smallest circle containing the target region.

[0041] Polar coordinate transformation: conversion of rectangular coordinates to polar coordinates.

[0042] The application discloses a method and device for detecting the rubber coating condition of the end face of a cylindrical battery cell, an electronic device and a computer readable storage medium, and relates to the technical field of industrial vision detection. The method comprises the following steps: acquiring a target end face rubber coating image of a cylindrical battery cell to be detected; performing insulation sheet detection processing on the target end face rubber coating image to obtain an insulation sheet detection result; when the insulation sheet detection result is that the insulation sheet exists, performing shielding area detection processing on the target end face rubber coating image to obtain a target fan-shaped shielding area; performing rubber coating area detection processing on the target end face rubber coating image to obtain an initial rubber coating area; subtracting the target fan-shaped shielding area from the initial rubber coating area to obtain a rubber coating effective detection area; performing radial scanning detection judgment processing on the rubber coating effective detection area to obtain a plurality of target rubber coating detection areas; and performing continuity defect detection processing on the plurality of target rubber coating detection areas to obtain a rubber coating defect detection result. The method can improve the effective coverage rate and reliability of the detection of the rubber coating condition of the end face of the cylindrical battery cell.

[0043] The embodiments of the application will be further described below with reference to the accompanying drawings.

[0044] In a first aspect, as Figure 1As shown, the method for detecting the encapsulation condition of the end face of the cylindrical battery cell can include but is not limited to steps S110 to S170.

[0045] Step S110: Obtain a target end face encapsulation image of the cylindrical battery cell to be detected.

[0046] Step S120: Perform insulation sheet detection processing on the target end face encapsulation image to obtain an insulation sheet detection result.

[0047] Step S130: When the insulation sheet detection result is that there is an insulation sheet, perform shielding area detection processing on the target end face encapsulation image to obtain a target fan-shaped shielding area; the target fan-shaped shielding area includes an extension handle of the end face of the cylindrical battery cell.

[0048] Step S140: Perform encapsulation area detection processing on the target end face encapsulation image to obtain an initial encapsulation area.

[0049] Step S150: Subtract the target fan-shaped shielding area from the initial encapsulation area to obtain an encapsulation effective detection area.

[0050] Step S160: Perform radial scanning detection judgment processing on the encapsulation effective detection area to obtain a plurality of target encapsulation detection areas.

[0051] Step S170: Perform continuity defect detection processing on the plurality of target encapsulation detection areas to obtain an encapsulation defect detection result.

[0052] Specifically, in step S110, the end face of the cylindrical battery cell is shown in the schematic diagram as Figure 2 The end face of the cylindrical battery cell includes: a battery cell, an insulation sheet, an extension handle, and an encapsulation part.

[0053] Through steps S110 to S170, in the process of detecting the encapsulation condition of the end face of the cylindrical battery cell, first, an encapsulation image of a target end face of the cylindrical battery cell to be detected is obtained; second, insulation sheet detection processing is performed on the encapsulation image of the target end face to obtain an insulation sheet detection result; third, when the insulation sheet detection result is that the insulation sheet exists, shielding area detection processing is performed on the encapsulation image of the target end face to obtain a target fan-shaped shielding area; the target fan-shaped shielding area includes an extension handle of the end face of the cylindrical battery cell; the target fan-shaped shielding area is an area that will interfere with the encapsulation condition detection, and the target fan-shaped shielding area lays a foundation for subsequent determination of a target encapsulation detection area, so as to improve the effective coverage rate of the encapsulation detection; fourth, encapsulation area detection processing is performed on the encapsulation image of the target end face to obtain an initial encapsulation area; fifth, the initial encapsulation area is subtracted by the target fan-shaped shielding area to obtain an encapsulation effective detection area; the target fan-shaped shielding area that will interfere with the encapsulation condition detection is removed from the initial encapsulation area to obtain the encapsulation effective detection area, so as to improve the effective coverage rate of the encapsulation detection; sixth, radial scanning detection judgment processing is performed on the encapsulation effective detection area to obtain a plurality of target encapsulation detection areas; and seventh, continuity defect detection processing is performed on the plurality of target encapsulation detection areas to obtain an encapsulation defect detection result. The target encapsulation detection area can be used to quickly and reliably detect the encapsulation condition of the end face of the cylindrical battery cell. After the invalid target fan-shaped shielding area is removed, the encapsulation condition of the end face of the cylindrical battery cell is quickly and reliably detected based on the determined encapsulation effective detection area, and the effective coverage rate and reliability of the encapsulation condition detection are improved. That is, the encapsulation condition detection of the end face of the cylindrical battery cell can improve the effective coverage rate and reliability.

[0054] According to some embodiments of the present application, step S110 is further described as follows: step S110: obtaining an encapsulation image of a target end face of a cylindrical battery cell to be detected, including but not limited to steps S111 to S112.

[0055] Step S111: obtaining an initial encapsulation image of an end face of a cylindrical battery cell.

[0056] Step S112: performing image preprocessing on the initial encapsulation image of the end face to obtain an encapsulation image of a target end face; wherein the image preprocessing includes color channel separation processing; and the encapsulation image of the target end face includes a red channel component image, a green channel component image, and a blue channel component image.

[0057] Specifically, step S110 further includes determining a reference position of the cylindrical battery cell through template matching.

[0058] Specifically, the initial encapsulation image of the end face is a color image to be processed. The side face of the cylindrical battery cell can be captured by a camera device to obtain the initial encapsulation image of the end face.

[0059] Specifically, the step S112 comprises: performing color channel separation processing on the inputted initial end face rubber coating image of color to obtain a red channel component image, a green channel component image and a blue channel component image.

[0060] In some embodiments, the image preprocessing further comprises: color space conversion processing. Specifically, the obtained red channel component image, green channel component image and blue channel component image can be further subjected to color space conversion according to actual needs.

[0061] Through the steps S111 to S112, the color channel separation processing is performed on the collected initial end face rubber coating image to obtain the red channel component image, the green channel component image and the blue channel component image, thereby laying a data foundation for subsequent insulating sheet detection processing and shielding area detection processing.

[0062] According to some embodiments of the present application, the step S120 is further illustrated, and the step S120 comprises: performing insulating sheet detection processing on the target end face rubber coating image to obtain an insulating sheet detection situation, including but not limited to the steps S121 to S125.

[0063] The step S121 comprises: obtaining the red channel component image from the target end face rubber coating image.

[0064] The step S122 comprises: performing binarization processing on the red channel component image to obtain a first binarization image.

[0065] The step S123 comprises: performing region feature analysis processing on the first binarization image to extract connected regions in the first binarization image.

[0066] The step S124 comprises: performing gray scale feature statistics on each connected region to obtain gray scale information.

[0067] The step S125 comprises: performing comparison processing on the gray scale information and gray scale judgment threshold information, and determining the insulating sheet detection situation according to the obtained comparison result.

[0068] Specifically, in the step S121, based on the features of each channel separation image, the red channel component image Ir(x, y) is selected for insulating sheet detection processing to determine whether there is an insulating sheet.

[0069] Specifically, the step S122 comprises: setting gray scale threshold information, the gray scale threshold information comprising: an upper threshold and a lower threshold; and performing binarization processing on the selected red channel component image Ir(x, y) based on the gray scale threshold information to obtain a first binarization image, thereby laying a data foundation for subsequent region feature analysis processing.

[0070] Specifically, in the step S124, the gray scale information comprises: average gray scale intensity and gray scale standard deviation.

[0071] Specifically, in step S125, the grayscale judgment threshold information includes a grayscale intensity threshold and a standard deviation threshold; wherein the determining the insulation sheet detection condition according to the obtained comparison result includes: when the average grayscale intensity is less than the grayscale intensity threshold, and the standard deviation threshold is less than the standard deviation threshold, it is judged that there is no insulation sheet; otherwise, it is judged that there is an insulation sheet; and the insulation sheet existence judgment is completed.

[0072] It should be noted that when it is judged that there is no insulation sheet, the encapsulation condition detection of the cylindrical cell end face is terminated; and when it is judged that there is an insulation sheet, subsequent steps S130 to S170 are continued.

[0073] Through steps S121 to S125, the insulation sheet detection extracts the potential area of the insulation sheet, and the insulation sheet existence judgment is completed, providing a reliable reference for whether to continue the shielding area detection processing. Moreover, in the insulation sheet detection stage, the system uses an adaptive threshold segmentation algorithm, effectively overcoming the false detection problem caused by the film reflection, and the detection accuracy is significantly improved compared with the traditional fixed threshold method.

[0074] According to some embodiments of the present application, step S130 is further described as follows: step S130: performing shielding area detection processing on the target end face encapsulation image to obtain a target fan-shaped shielding area, including but not limited to steps S131 to S134.

[0075] Step S131: performing image difference processing on the red channel component image and the green channel component image obtained from the target end face encapsulation image to obtain a first difference image.

[0076] Step S132: performing image coarse positioning enhancement processing on the first difference image to obtain an enhanced circular coarse positioning area.

[0077] Step S133: performing polar coordinate establishment processing and polar coordinate analysis processing based on the enhanced circular coarse positioning area to obtain a maximum angle interval.

[0078] Step S134: performing shielding area generation processing and hole filling processing according to the maximum angle interval to obtain the target fan-shaped shielding area.

[0079] Specifically, step S131 includes: performing weighted difference operation on the red channel component image Ir(x, y) and the green channel component image Ig(x, y) to obtain the first difference image.

[0080] Specifically, in step S132, the image coarse positioning enhancement processing includes: a binarization processing, a coarse positioning region extraction processing, a ROI-based difference image cropping processing, and an image enhancement processing, which are sequentially performed. Specifically, step S132 includes but is not limited to steps S1321 to S1324.

[0081] In step S1321, a threshold range is set, and a threshold segmentation and binarization processing is performed based on the threshold range to obtain a second binarization image.

[0082] In step S1322, a coarse positioning region extraction processing is performed: a circular region extraction operation is performed on the second binarization image to extract a maximum connected region in the binarization image; and a minimum circumscribed circle algorithm is used to calculate a region center coordinate and a radius R in the maximum connected region, to obtain a circular coarse positioning region with the region center coordinate as a center O and the radius R. Wherein, is a row (Row) pixel coordinate, indicating a vertical position (from top to bottom) of a point in an image; represents a column (Column) pixel coordinate, indicating a horizontal position (from left to right) of a point in an image.

[0083] In step S1323, a ROI-based difference image cropping processing is performed to obtain a circular coarse positioning region. It can be understood that the ROI here refers to the extracted circular region.

[0084] In step S1324, an image enhancement processing is performed: after a circular structural element S is defined, a gray scale dilation operation is performed on the circular coarse positioning region obtained by cropping based on the circular structural element S, to obtain an enhanced circular coarse positioning region.

[0085] Specifically, in step S133, a polar coordinate establishment processing and a polar coordinate analysis processing are performed based on the enhanced circular coarse positioning region to obtain a maximum angle interval, including steps S1331 to S1333.

[0086] In step S1331, a binarization processing is performed again on the enhanced circular coarse positioning region to obtain a third binarization image, and a candidate region is further determined from the third binarization image; a minimum circumscribed circle is calculated for the candidate region, and a set of center coordinates of each minimum circumscribed circle is recorded; wherein i represents the i-th minimum circumscribed circle; is a pixel-level center coordinate of the i-th minimum circumscribed circle.

[0087] In step S1332, the center O of the circular coarse positioning region (i.e., the above-mentioned region center coordinate ) to establish a polar coordinate system for the polar coordinate origin; in the established polar coordinate system, according to mathematical calculation common sense, polar coordinate transformation is realized to convert each pixel-level center coordinates in the center coordinates set of the minimum circumscribed circle into polar coordinates ; wherein, according to the polar angle in each polar coordinate, an angle set is generated.

[0088] After the polar angles in the angle set are sorted in size order , the maximum value and the minimum value of the polar angles are taken to perform a difference operation, to obtain the maximum angle interval .

[0089] Specifically, step S134: performing shielding region generation processing and hole filling processing according to the maximum angle interval to obtain the target sector shielding region includes steps S1341 to S1342.

[0090] Step S1341: the shielding region generation processing is specifically: taking the maximum angle interval as the center reference, to generate the target sector shielding region .

[0091] ;

[0092] wherein, (x, y) is the image pixel coordinate; is the reference center coordinate (polar coordinate origin coordinate); is the shielding central angle of the target sector shielding region (i.e. the maximum angle interval mentioned above); and ±45° is the shielding zone opening angle; is the polar coordinate angle calculation.

[0093] Step S1342: simultaneously performing hole filling processing on the generated target sector shielding region to obtain the final target sector shielding region.

[0094] As shown in FIG. 13, an example is given to illustrate the algorithm flow of generating the target sector shielding region according to the embodiments of the present application. The algorithm for generating the target sector shielding region mainly includes: inputting the image to be processed, reference center positioning, ring feature region extraction, feature point detection and screening, establishing polar coordinate mapping, angle distribution statistical analysis, determining the maximum angle interval, calculating the shielding region boundary, calculating the target sector shielding region and outputting. Figure 3 The target sector shielding region is a region that will interfere with the encapsulation condition detection. Through steps S131 to S134, the target sector shielding region is determined to lay a foundation for subsequent determination of the target encapsulation detection region, so as to improve the effective coverage rate of the encapsulation detection.

[0095] ​​

[0096] According to some embodiments of the present application, step S140 is further illustrated as follows: step S140: performing a rubberizing area detection processing on the target end face rubberizing image to obtain an initial rubberizing area, including but not limited to steps S141 to S144.

[0097] Step S141: obtaining a green channel component image and a blue channel component image from the target end face rubberizing image.

[0098] Step S142: performing a second difference processing on the green channel component image and the blue channel component image to obtain a second difference image.

[0099] Step S143: performing a morphological filtering processing on the second difference image to obtain a filtered second difference image.

[0100] Step S144: performing a binarization processing, a connected region extraction processing and an area feature-based screening processing on the filtered second difference image to obtain the initial rubberizing area.

[0101] Specifically, the second difference processing is a multi-channel difference processing; through steps S141 to S142, a weighted difference operation is performed on the green channel component image Ib(x, y) and the blue channel component image Ig(x, y) to obtain a second difference image; which lays a data foundation for obtaining the initial rubberizing area subsequently.

[0102] In step S143, the morphological filtering processing on the second difference image specifically refers to: after defining a circular structural element S, performing a gray open operation on the second difference image; to obtain the filtered second difference image.

[0103] Specifically, step S144 includes but is not limited to steps S1441 to S1443.

[0104] Step S1441: performing a binarization processing on the filtered second difference image to obtain a fourth binarization image.

[0105] Step S1442: performing a connected region extraction processing on the fourth binarization image to obtain a plurality of connected regions, and obtaining area features of each connected region.

[0106] Step S1443: according to the area features, sorting in descending order of area, and screening a connected region with a relatively large area as the initial rubberizing area.

[0107] After obtaining the initial rubberizing area through steps S141 to S1443, in order to remove a target fan-shaped shielding area in the initial rubberizing area which will interfere with the rubberizing condition detection, obtain a rubberizing effective detection area, and thus improve the effective coverage rate of the rubberizing detection.

[0108] As an example, step S150 is further explained. In step S150, a set difference operation of the encapsulation region is performed to obtain an effective detection region of encapsulation :

[0109] ;

[0110] wherein, is a pixel set of the original encapsulation region (i.e., the initial encapsulation region), is a target sector shielding region calculated in advance; is the effective detection region of encapsulation.

[0111] According to some embodiments of the present application, step S160 is further explained, and step S160 is: performing a radial scanning detection judgment process on the effective detection region of encapsulation to obtain a plurality of target encapsulation detection regions, including but not limited to steps S161 to S164.

[0112] In step S161, the effective detection region of encapsulation obtained through step S150 is subjected to radial scanning detection, as shown in FIG. 6, and the radial scanning principle is: taking the encapsulation center of the effective detection region of encapsulation as the origin, then N detection lines are generated along the radial direction, and the adjacent two detection lines are separated by a preset angle. Figure 4 After the radial scanning detection is completed, the end point coordinates of each detection line (radial scanning coordinate calculation formula) are obtained as follows:

[0113] ;

[0114] wherein, R is the minimum circumscribed circle radius generated through the coarse positioning region, and 150 is the radius expansion amount.

[0115] In step S162, the intersection region of the detection line and the effective detection region of encapsulation is obtained; it can be understood that N detection lines and the effective detection region of encapsulation exist a plurality of intersection regions, and i represents the i-th intersection region.

[0116] In step S163, the minimum circumscribed rectangle parameters of each intersection region are calculated, and specifically, the minimum circumscribed rectangle parameters include: the center coordinates, the direction angle, and the half side length and ​​​​​​​​​​​​​​​

[0117] Step S164, determining the intersection region of the main half-axis length L1>0 as an effective detection region, and storing detection data for each effective detection region And .

[0118] Through steps S161 to S164, after excluding interference, a plurality of target encapsulation detection regions are obtained, laying a data foundation for subsequent continuity defect detection processing.

[0119] According to some embodiments of the present application, step S170 is further described, step S170: performing continuity defect detection processing on the plurality of target encapsulation detection regions to obtain encapsulation defect detection results, including but not limited to steps S171 to S174.

[0120] Step S171: sequentially traversing the target encapsulation detection regions, determining a target encapsulation detection region with an encapsulation width less than a preset width threshold as a width abnormal position, and generating an abnormal position index set according to a plurality of width abnormal positions.

[0121] Step S172: sequentially traversing the width abnormal positions in the abnormal position index set, performing continuity interval detection recording processing according to a preset minimum continuous defect point number to obtain a continuous defect interval; the number of width abnormal positions included in the continuous defect interval is greater than the minimum continuous defect point number.

[0122] Step S173: when the number of continuous defect intervals is not zero, determining that the encapsulation defect detection result is that there is a continuous defect interval.

[0123] Step S174: when the number of continuous defect intervals is zero, determining that the encapsulation defect detection result is that there is no continuous defect interval.

[0124] Step S171 realizes defect region identification. An example is given to illustrate step S171:

[0125] First, set the preset width threshold T=X pixels; specifically, the classical value of X is 100 pixels, which is dynamically adjusted according to process requirements. Then, traverse all detection positions and record the abnormal position index set:

[0126] ;

[0127] Wherein, is the abnormal position index set, which can also be understood as the abnormal scan line index set. is the encapsulation width in the radial direction on the ith scan line, and N is the total number of detection points.

[0128] Step S172 realizes continuity defect determination. An example is given to illustrate step S172:

[0129] First, initialize the counter, set the minimum number of consecutive defect points m, the current continuous length L to 0. Then, traverse the abnormal position index set : When the adjacent index satisfies : Let L = L + 1. Finally, when L ≥ m, record the continuous defect interval: ; Wherein, is the continuous defect interval, which is also the continuous defect index set. Specifically, if m = 6, when L ≥ 6, i.e. 6 consecutive point detection position abnormalities trigger an alarm.

[0130] Through steps S171 to S174, the encapsulation condition of the end face of the cylindrical battery cell is quickly and reliably detected based on the determined encapsulation effective detection area, and the effective coverage rate and reliability of the encapsulation condition detection are improved.

[0131] According to some embodiments of the present application, after step S170, i.e. after obtaining the encapsulation defect detection result, the encapsulation condition detection method further includes but is not limited to steps S210 to S220.

[0132] Step S210: When the encapsulation defect detection result is: there is a continuous defect interval, mark and visually display the continuous defect interval.

[0133] Step S220: When the encapsulation defect detection result is: there is no continuous defect interval, perform size calculation processing on the end face encapsulation of the cylindrical battery cell to obtain the average encapsulation thickness.

[0134] Specifically, in step S210, in an embodiment, when there is a continuous defect interval, the continuous defect interval is merged, the defect is marked, and the entire defect area corresponding to the continuous defect interval is visually displayed.

[0135] Specifically, in step S220, when performing size calculation processing, the calculation formula of the average encapsulation thickness used is: ; end the overall algorithm, and output: qualification determination and average encapsulation thickness .

[0136] Through steps S210 to S220, different subsequent processing is performed according to different encapsulation defect detection results, so that engineering personnel can timely learn about the encapsulation defect detection results and related situations.

[0137] In combination with Figure 5 , the overall flow of the encapsulation condition detection method for the end face of the cylindrical battery cell provided by an embodiment of the present application is further described.

[0138] Step S501: input the initial end face encapsulation image of the cylindrical battery cell.

[0139] Step S502: color channel separation is performed on the initial end face coating image to obtain a target end face coating image.

[0140] Step S503: insulation sheet detection is performed based on the target end face coating image, and it is determined whether the insulation sheet detection exists, if not, jump to step S504; if yes, jump to step S505.

[0141] Step S504: end the detection.

[0142] Step S505: image difference processing, image coarse positioning enhancement processing, and image coarse positioning enhancement processing are performed based on the target end face coating image to obtain an enhanced circular coarse positioning region.

[0143] Step S506: polar coordinate establishment processing and polar coordinate analysis processing are performed on the enhanced circular coarse positioning region to obtain a maximum angle interval.

[0144] Step S507: a target sector shielding region is generated based on the maximum angle interval.

[0145] Step S508: coating region detection processing is performed on the target end face coating image to obtain an initial coating region, and the initial coating region is subtracted from the target sector shielding region to obtain a coating effective detection region.

[0146] Step S509: radial scanning detection judgment and continuity defect detection are performed based on the coating effective detection region.

[0147] Step S510: it is determined whether there is a defect, if yes, step S511 is performed; if not, step S512 is performed.

[0148] Step S511: size calculation processing is performed on the end face coating of the cylindrical battery cell to obtain an average coating thickness.

[0149] Step S512: the defect region is marked and visualized.

[0150] It needs to be emphasized that there are deficiencies in the existing algorithm: insufficient suppression ability to reflective interference; fixed detection parameters, poor adaptability; insufficient recognition accuracy of local defects; in the detection process, a static rectangular shielding window is used to avoid interference of fixed structures such as extended handles, and the position and size of the window need to be manually preset; defect judgment relies on single width threshold comparison, and lacks analysis of defect morphology and distribution regularity. The present application proposes a feature extraction method based on data quantization, which fundamentally reconstructs the logical architecture of traditional detection methods through data quantization visual analysis technology, and converts subjective experience driven feature judgment into an objective data quantization scientific decision system. In terms of interference elimination, the algorithm deeply combines the product characteristics of cylindrical battery cells, and builds a layered suppression mechanism for the core interference sources of film reflective light and extended handle: first, a reflective light suppression mechanism is established based on material properties; second, a dynamic reference coordinate system correction technology is used to track the geometric features of the cylindrical battery cell end face in real time; and finally, according to the detection data screening, the systematic errors caused by process fluctuations are effectively eliminated.

[0151] In summary, the embodiment of the present application first adopts a multi-channel collaborative analysis strategy, combines red channel gray scale statistics and red-blue difference processing, blue-green channel difference processing, effectively overcomes the reflective interference problem in the detection of insulating sheets in traditional methods; second, through the polar coordinate dynamic shielding mechanism, the optimal shielding area is automatically generated based on the feature circle distribution analysis, which greatly improves the effective coverage rate of the encapsulation detection. The three-level progressive detection architecture adopted by the algorithm realizes fast screening, and the parameterized design makes the key threshold dynamically adjustable, which can effectively overcome the reflective interference and improve the detection reliability. The radial scanning strategy optimizes the stability while ensuring the detection accuracy, and the continuous defect judgment logic effectively balances the false rejection rate and the missed detection rate. The technical scheme realizes the existence judgment and encapsulation quality detection of the insulating sheet while maintaining the detection speed of milliseconds, which fully matches the high-speed rhythm requirements of the cylindrical battery production line. It can be seen that the cylindrical battery cell end surface insulating sheet and encapsulation quality detection algorithm proposed in the present application realizes significant improvement in detection ability and stability through innovative multi-level detection architecture.

[0152] As shown in Figure 6 The present application also provides a device for detecting the encapsulation condition of the cylindrical battery cell end surface, comprising:

[0153] The processor 601 can be implemented in a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute related programs to realize the technical solutions provided by the embodiments of the present application;

[0154] The memory 602 can be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM), etc. The memory 602 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 602 and are called and executed by the processor 601 to implement the method for detecting the encapsulation condition of the end face of the cylindrical battery cell provided by the embodiments of the present application;

[0155] The input / output interface 603 is configured to realize information input and output.

[0156] The communication interface 604 is configured to realize the communication interaction between the device and other devices, and the communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0157] The bus 605 is configured to transmit information between various components (for example, the processor 601, the memory 602, the input / output interface 603, and the communication interface 604) of the device.

[0158] The processor 601, the memory 602, the input / output interface 603, and the communication interface 604 are connected to each other through the bus 605 to realize the communication connection between the device.

[0159] The embodiments of the present application also provide an electronic device, which comprises the device for detecting the encapsulation condition of the end face of the cylindrical battery cell as described above.

[0160] The embodiments of the present application also provide a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program. The computer program is executed by a processor to implement the method for detecting the encapsulation condition of the end face of the cylindrical battery cell.

[0161] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, which can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The above-described device embodiments are merely illustrative, and units described as separate components can or can not be physically separated, implemented in one place, or distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0162] Those of ordinary skill in the art will understand that all or some of the steps in the above-disclosed methods can be implemented as software, firmware, hardware, or a suitable combination thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer-readable media, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery medium.

[0163] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the present application.

Claims

1. A method for detecting the encapsulation condition of the end face of a cylindrical battery cell, characterized by, The method comprises the following steps: acquire a target end face rubber coating image of a cylindrical battery cell to be detected; perform insulation sheet detection processing on the target end face rubber coating image to obtain an insulation sheet detection result; when the insulation sheet detection result is that an insulation sheet exists, perform shielding area detection processing on the target end face rubber coating image to obtain a target fan-shaped shielding area; the target fan-shaped shielding area includes an extension handle of the end face of the cylindrical battery cell; perform rubber coating area detection processing on the target end face rubber coating image to obtain an initial rubber coating area; subtract the target fan-shaped shielding area from the initial rubber coating area to obtain a rubber coating effective detection area; perform radial scanning detection judgment processing on the rubber coating effective detection area to obtain a plurality of target rubber coating detection areas; perform continuity defect detection processing on the plurality of target rubber coating detection areas to obtain a rubber coating defect detection result.

2. The method of claim 1, wherein the method further comprises: The method comprises the following steps: acquire an initial end face rubber coating image of the cylindrical battery cell; perform image preprocessing on the initial end face rubber coating image to obtain a target end face rubber coating image; the image preprocessing comprises color channel separation processing; the target end face rubber coating image comprises a red channel component image, a green channel component image, and a blue channel component image.

3. The method of claim 2, wherein the method further comprises: The method comprises the following steps: acquire a red channel component image from the target end face rubber coating image; perform binaryzation processing on the red channel component image to obtain a first binaryzation image; perform region feature analysis processing on the first binaryzation image to extract connected regions in the first binaryzation image; perform gray scale feature statistics on each connected region to obtain gray scale information; perform comparison processing on the gray scale information and gray scale judgment threshold information, and determine the insulation sheet detection result according to the obtained comparison result.

4. The method of claim 2, wherein the method further comprises: The method comprises the following steps: perform image difference processing on the red channel component image and the green channel component image acquired from the target end face rubber coating image to obtain a first difference image; perform image coarse positioning enhancement processing on the first difference image to obtain an enhanced circular coarse positioning area; perform polar coordinate establishment processing and polar coordinate analysis processing based on the enhanced circular coarse positioning area to obtain a maximum angle interval; perform shielding area generation processing and hole filling processing according to the maximum angle interval to obtain a target fan-shaped shielding area.

5. The method of claim 2, wherein the method further comprises: The method comprises the following steps: acquire a green channel component image and a blue channel component image from the target end face rubber coating image; perform second difference processing on the green channel component image and the blue channel component image to obtain a second difference image; perform morphological filtering processing on the second difference image to obtain a filtered second difference image; perform binaryzation processing, connected region extraction processing, and area feature-based screening processing on the filtered second difference image to obtain an initial rubber coating area.

6. The method of claim 2, wherein the method further comprises: The continuity defect detection processing is performed on the plurality of target encapsulation detection areas to obtain an encapsulation defect detection result, and the encapsulation defect detection result is obtained by performing the continuity defect detection processing on the plurality of target encapsulation detection areas, including: The target encapsulation detection areas with an encapsulation width less than a preset width threshold are determined as width abnormal positions by sequentially traversing the target encapsulation detection areas, and an abnormal position index set is generated according to the plurality of width abnormal positions; The width abnormal positions in the abnormal position index set are sequentially traversed, and a continuous defect interval is obtained by performing a continuous interval detection recording processing according to a preset minimum continuous defect point number; the number of the width abnormal positions included in the continuous defect interval is greater than the minimum continuous defect point number; When the number of the continuous defect intervals is not zero, it is determined that the encapsulation defect detection result is that there is a continuous defect interval; When the number of the continuous defect intervals is zero, it is determined that the encapsulation defect detection result is that there is no continuous defect interval.

7. The method of claim 6, wherein the method further comprises: After the encapsulation defect detection result is obtained, the encapsulation condition detection method further includes: When the encapsulation defect detection result is that there is a continuous defect interval, the continuous defect interval is marked and visually displayed; When the encapsulation defect detection result is that there is no continuous defect interval, a size calculation processing is performed on the end face encapsulation of the cylindrical battery cell to obtain an average encapsulation thickness.

8. A device for detecting the encapsulation state of the end face of a cylindrical battery cell, characterized by The device includes at least one processor and a memory connected in communication with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the encapsulation condition detection method for the end face of the cylindrical battery cell according to any one of claims 1 to 7.

9. An electronic device, comprising: The device includes the encapsulation condition detection device for the end face of the cylindrical battery cell according to claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the encapsulation condition detection method for the end face of the cylindrical battery cell according to any one of claims 1 to 7.

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