Lithium battery end face defect detection method and device, electronic equipment and medium

Abstract

The invention discloses a lithium battery end face defect detection method and device, electronic equipment and a medium. The method comprises the steps that a lithium battery end face image corresponding to a to-be-detected lithium battery is acquired; and performing defect detection on the lithium battery end face image according to a preset defect detection type, and determining a corresponding defect detection result. According to the invention, automatic lithium battery end face defect detection can be realized, the efficiency, stability and accuracy of lithium battery end face defect detection are improved, the high-speed and high-accuracy detection requirements of modern industry are met, and the method can be widely applied to the technical field of battery detection.

Description

Technical Field

[0001] The present application relates to the field of battery detection technology, and in particular to a method, device, electronic equipment and medium for detecting end surface defects of a lithium battery. Background Art

[0002] As a common energy storage device, lithium batteries are widely used in various fields of production and life, such as household appliances, electronic products, electronic instruments, automation equipment, mobile phones, computers, new energy vehicles, etc. With the rapid development of the manufacturing industry, people's requirements for battery quality are increasing day by day. The surface defects of battery products will directly affect the safety of consumers. The battery shell is an important part of the battery product, and its quality determines the quality of the battery product.

[0003] At present, manual visual inspection is mainly used to complete defect detection of lithium batteries. This method has the disadvantages of strong subjectivity, slow detection speed, low detection efficiency, and large uncertainty. It is difficult to meet the high-speed and high-accuracy detection requirements of modern industry. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a lithium battery end face defect detection method, device, electronic equipment and medium, which can realize automated lithium battery end face defect detection and improve the efficiency and accuracy of lithium battery end face defect detection.

[0005] On the one hand, the embodiment of the present application provides a method for detecting end surface defects of a lithium battery, the method comprising the following steps:

[0006] Acquire a lithium battery end face image corresponding to the lithium battery to be tested;

[0007] According to the preset defect detection type, defect detection is performed on the lithium battery end face image to determine the corresponding defect detection result.

[0008] In some embodiments, obtaining the lithium battery end face image corresponding to the lithium battery to be detected specifically includes:

[0009] Obtaining an image of the positive terminal surface of the battery cell and an image of the negative terminal surface of the battery cell of the lithium battery to be tested;

[0010] The battery cell positive terminal face image and the battery cell negative terminal face image are subjected to image preprocessing to obtain a corresponding first end face image and a second end face image, wherein the first end face image corresponds to the battery cell positive terminal face image, and the second end face image corresponds to the battery cell negative terminal face image.

[0011] In some embodiments, the defect detection type includes multiple defect detection subcategories, and the defect detection is performed on the lithium battery end face image according to the preset defect detection type to determine the corresponding defect detection result, specifically including:

[0012] Obtaining the defect detection strategy corresponding to each of the defect detection subclasses;

[0013] According to each of the defect detection strategies, defect detection is performed on the first end surface image to determine a first defect detection result corresponding to each of the defect detection subclasses;

[0014] According to each of the defect detection strategies, defect detection is performed on the second end surface image to determine a second defect detection result corresponding to each of the defect detection subclasses;

[0015] The defect detection result is determined according to the first defect detection result and the second defect detection result corresponding to each of the defect detection subclasses.

[0016] In some embodiments, the defect detection types include at least tab eversion defect detection, end face center hole defect detection, foreign object obstruction defect detection, tab inversion defect detection, tab blackening defect detection and tab deformation defect detection.

[0017] In some embodiments, performing defect detection on the first end surface image according to each of the defect detection strategies to determine the first defect detection result corresponding to each of the defect detection subclasses specifically includes:

[0018] Performing end face area positioning on the first end face image, and extracting a corresponding end face positioning image from the first end face image;

[0019] According to each of the defect detection strategies, defect detection is performed on the end face positioning image to determine the first defect detection result corresponding to each of the defect detection subclasses.

[0020] In some embodiments, the image preprocessing of the battery cell positive end surface image and the battery cell negative end surface image to obtain the corresponding first end surface image and second end surface image specifically includes:

[0021] The battery cell positive end surface image and the battery cell negative end surface image are sequentially subjected to image grayscale processing, image background filtering, image filtering and enhancement to generate the corresponding first end surface image and second end surface image.

[0022] In some embodiments, the method further comprises:

[0023] Classifying the lithium battery to be detected according to the defect detection result, and determining a target defect detection classification corresponding to the lithium battery to be detected;

[0024] According to the target defect detection classification, a corresponding lithium battery processing scheme is determined, and the lithium battery to be detected is processed according to the lithium battery processing scheme.

[0025] On the other hand, an embodiment of the present application provides a lithium battery end surface defect detection device, the device comprising:

[0026] The first module is used to obtain a lithium battery end face image corresponding to the lithium battery to be tested;

[0027] The second module is used to perform defect detection on the lithium battery end face image according to a preset defect detection type and determine the corresponding defect detection result.

[0028] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the lithium battery end face defect detection method described above when executing the computer program.

[0029] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned lithium battery end face defect detection method is implemented.

[0030] The embodiments of the present application include at least the following beneficial effects: The present application provides a lithium battery end face defect detection method, device, electronic device and medium, which obtains a lithium battery end face image corresponding to the lithium battery to be detected, performs defect detection on the lithium battery end face image according to a preset defect detection type, and determines the corresponding defect detection result. The present application can realize automated lithium battery end face defect detection, improve the efficiency, stability and accuracy of lithium battery end face defect detection, and meet the high-speed and high-accuracy detection requirements of modern industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0033] Figure 1 is a flow chart of a lithium battery end face defect detection method provided in an embodiment of the present application;

[0034] Figure 2is a schematic diagram of an image of the negative terminal surface of a battery cell in an embodiment of the present application;

[0035] Figure 3 is a schematic diagram of completing the grayscale processing of the negative terminal surface image of the battery cell in the embodiment of the present application;

[0036] Figure 4 is a schematic diagram of completing image preprocessing of the negative terminal surface image of the battery cell in an embodiment of the present application;

[0037] Figure 5 is a schematic diagram of an end surface region prod_reg corresponding to the second end surface image in an embodiment of the present application;

[0038] Figure 6 is a schematic diagram of the inner and outer circle areas corresponding to the second end surface image in an embodiment of the present application;

[0039] Figure 7 is a schematic diagram of image_b_reduced in an embodiment of the present application;

[0040] Figure 8 is a schematic diagram of a center hole image corresponding to a second end surface image in an embodiment of the present application;

[0041] Fig. 9 is a schematic diagram of the central hole area corresponding to the second end surface image in an embodiment of the present application;

[0042] Fig.10 It is a structural schematic diagram of a lithium battery end face defect detection device provided in an embodiment of the present application;

[0043] Fig.11 It is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] 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 conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0045] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0046] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0048] After the battery cell is flattened, there are various defects. The defects on the positive and negative terminals are of the same type and similar in shape, such as the tabs are turned outward, the tabs are turned inward, the center hole of the tabs is unqualified, covered by foreign matter, the tabs are black, and there are serious NGs. Due to the complex texture of the end surface and the low contrast of the defects, it is difficult to achieve accurate detection. At present, the end surface of the battery cell is mainly inspected by manual visual inspection or random inspection. The degree of automation is low, the defect size is difficult to quantify, and the judgment is very subjective.

[0049] Based on this, the embodiments of the present application propose a lithium battery end face defect detection method, device, electronic device and medium, which can perform online real-time detection of each battery cell end face, accurately detect various defects on the positive and negative end faces of the battery cell, have high detection efficiency and accuracy, and realize efficient and non-destructive detection.

[0050] Reference Figure 1 , Figure 1 This is an optional flow chart of a lithium battery end face defect detection method provided in an embodiment of the present application. The method may include but is not limited to steps S101 to S102:

[0051] Step S101, obtaining a lithium battery end surface image corresponding to the lithium battery to be tested;

[0052] Step S102, performing defect detection on the lithium battery end surface image according to a preset defect detection type, and determining a corresponding defect detection result.

[0053] In some embodiments, step S101 may include but is not limited to steps S201 to S202:

[0054] Step S201, obtaining an image of the positive terminal surface of a battery cell and an image of the negative terminal surface of a battery cell of a lithium battery to be tested;

[0055] Step S202, preprocessing the positive end face image and the negative end face image of the battery cell to obtain corresponding first end face image and second end face image, wherein the first end face image corresponds to the positive end face image of the battery cell, and the second end face image corresponds to the negative end face image of the battery cell.

[0056] In some embodiments, a visual system is used to take an image of the end face of a lithium battery cell. Optionally, the visual system includes a ring light source, an area array camera, a camera lens, an industrial computer, a light source controller, and related cables.

[0057] Specifically, the process of taking a cell end face image is as follows:

[0058] The first step is to place the lithium battery cells on a V-shaped carrier and transport them to the inspection station via a conveyor line;

[0059] In the second step, when the lithium battery cell arrives at the inspection station, the photoelectric sensor of the visual system triggers the area array camera corresponding to the positive electrode to take a picture. At this time, the positive electrode light source is on and the negative electrode light source is off, completing the image of the positive end surface, that is, the above-mentioned image of the positive end surface of the battery cell. Then, the photoelectric sensor triggers the area array camera corresponding to the negative electrode to take a picture. At this time, the negative electrode light source is on and the positive electrode light source is off, completing the image of the negative end surface, that is, the above-mentioned image of the negative end surface of the battery cell.

[0060] In some embodiments, image preprocessing includes image grayscale processing and image filtering and enhancement. Specifically, the positive terminal surface image of the battery cell and the negative terminal surface image of the battery cell are sequentially subjected to image grayscale processing and image filtering and enhancement to generate corresponding first end surface image and second end surface image.

[0061] For example, taking the image preprocessing of the negative terminal surface image of the battery cell as an example, referring to Figure 2 , Figure 2 It is an optional schematic diagram of the negative end surface image of the battery cell in the embodiment of the present application. First, the negative end surface image of the battery cell is disassembled into three channel images, namely, a red channel image image_r, a green channel image image_g, and a blue channel image image_b. Then, the absolute value of the grayscale value of the green channel image image_b is subtracted from the red channel image image_r to form a new grayscale image image_diff, and the image grayscale processing is completed. Through the image grayscale processing, the shadow of the copper color in the same area on the negative end surface image of the battery cell can be eliminated, and the background in the field of view can be filtered out. At the same time, Figure 3As shown in the figure, the image_diff has higher image contrast and more uniform grayscale, which is very beneficial for accurate extraction of the end face area.

[0062] Then, the grayscale image image_diff is filtered and enhanced. For image_diff, a rectangular structure element 6×6 is used for median filtering, and then grayscale stretching is performed. The grayscale stretching coefficient is 4.0. After stretching, image_scaled is obtained. Image_scaled, that is, the second end face image mentioned above, is shown as Figure 4 As shown, image filtering and enhancement are completed. Image filtering and enhancement can reduce the impact of end face oxidation on subsequent end face area extraction, which is beneficial to the accurate extraction of the end face area.

[0063] In some embodiments, the defect detection type includes multiple defect detection subcategories, and step S102 may include but is not limited to steps S301 to S304:

[0064] Step S301, obtaining the defect detection strategy corresponding to each defect detection subclass;

[0065] Step S302, performing defect detection on the first end surface image according to each defect detection strategy, and determining a first defect detection result corresponding to each defect detection subclass;

[0066] Step S303, performing defect detection on the second end surface image according to each defect detection strategy, and determining a second defect detection result corresponding to each defect detection subclass;

[0067] Step S304: Determine a defect detection result according to the first defect detection result and the second defect detection result corresponding to each defect detection subclass.

[0068] In some embodiments, optionally, the defect detection types include at least multiple defect detection subcategories such as tab eversion defect detection, end face center hole defect detection, foreign object obstruction defect, tab defect detection, tab blackening defect detection and tab deformation defect detection.

[0069] In some embodiments, step S302 may include but is not limited to steps S401 to S402:

[0070] Step S401, performing end face region positioning on the first end face image, and extracting a corresponding end face positioning image from the first end face image;

[0071] Step S402: perform defect detection on the end face positioning image according to each defect detection strategy, and determine a first defect detection result corresponding to each defect detection subclass.

[0072] In some embodiments, the defect detection process of the second end face image is the same as the defect detection process of the first end face image, that is, the above-mentioned steps S401 to S402. Optionally, taking the defect detection of the first end face image as an example, first, the end face area in the first end face image is extracted, that is, the end face area is located, and the corresponding end face positioning image is obtained. Then, through regional feature analysis, serious end face defects such as lug eversion, lug deformation, large area occlusion, etc. are detected. Then, the end face positioning image is cropped, and the center hole area is extracted from the end face positioning image. Regional analysis is performed to detect the end face center hole defect, and then the defects of foreign object occlusion are detected. Finally, the lug eversion and blackening defects are detected.

[0073] In some embodiments, taking the end face area positioning of the second end face image as an example, first, the second end face image image_scaled is obtained, and image_scaled is segmented using a fixed threshold with a grayscale range of [160,255]. Regions with an area larger than a preset threshold are selected, and a circular structural element (such as a circle with a radius of 21.5) is used for a closing operation. Finally, the segmented region is filled to obtain the end face candidate region reg_closed.

[0074] Then, the end face candidate region reg_closed is judged and output. First, the area of ​​the end face candidate region reg_closed is calculated. If the area of ​​the end face candidate region reg_closed is less than or equal to the given area threshold, a lithium battery unqualified signal is output. If the area of ​​the end face candidate region reg_closed is greater than the given area threshold, the end face candidate region reg_closed is output as the end face region prod_reg, and the corresponding end face positioning image is generated, such as Figure 5 As shown, the area within the green circle is the extracted end surface area prod_reg.

[0075] In some embodiments, taking defect detection on the end face positioning image extracted from the second end face image as an example, first, the end face positioning image corresponding to the second end face image and including the end face area prod_reg is obtained, first, the roundness circ_val, the width and height of the circumscribed rectangle of the end face area prod_reg are calculated, and the aspect ratio rate_hw is obtained by dividing the height of the circumscribed rectangle by the width.

[0076] Severe defects on the end face include tab eversion, severe deformation, large-area obstruction by foreign objects, etc., which cause the contour shape of the end face area to be very different from normal. Severe defects on the end face are detected based on the roundness circ_val. If the roundness circ_val is less than the set value, such as 0.9, it is output that the lithium battery cell has severe defects on the end face. If the aspect ratio rate_hw is greater than the specified value, such as 1.2, or less than the specified value, such as 0.92, it is output that the lithium battery cell has severe defects on the end face.

[0077] Assuming that it is determined that the lithium battery cell has a serious end surface defect, the type of serious defect of the lithium battery cell is further determined as follows:

[0078] The first step is to determine whether there is an external object occlusion defect. First, the end face area prod_reg is eroded using a circular structure element with a radius of 40.5. The detection image is cut out based on the eroded area and image_diff.

[0079] Then, the detection image is extracted and judged. A fixed threshold is used for segmentation, the grayscale range is [0,36], and a rectangular structure element of 6×6 is used for opening operation to screen whether there is an area in the detection image that is larger than the preset external object occlusion area threshold. If it is determined that there is an area in the detection image that is larger than the external object occlusion area threshold, then the area is an external object occlusion area, and the output is that the lithium battery cell has an external object occlusion defect. Otherwise, the output is that the lithium battery cell does not have an external object occlusion defect.

[0080] In the second step, since the texture of the outer ring and inner ring of the end face of the battery cell is very different, it is divided into outer ring and inner ring detection. Under normal circumstances, the texture of the pole ear in the outer ring area is relatively sparse, and the texture of the pole ear in the inner ring area is very dense. Determine whether there is a pole ear inward defect or a pole ear outward defect. First, the region is intercepted according to the end face area prod_reg and the blue channel image image_b to obtain the intercepted image image_reduced, and the image grayscale is pulled up for image_reduced. The grayscale pulling coefficient is 2 to obtain the image_reduced after grayscale pulling. The image_reduced after grayscale pulling is median filtered using a rectangular structure element 11×11. Then, the automatic threshold segmentation method is used to segment the region to determine the dark area in the image_reduced after grayscale pulling. The image brightness of the dark area is lower than the preset image brightness threshold. The segmented area is filled, and then the circular structure element is used to perform an open operation according to the preset radius to obtain the inner circle area. The inner circle area is subtracted from the end face area prod_reg to obtain the outer circle area, such as Figure 6 As shown, area 1 is the inner circle area, and area 2 is the outer circle area;

[0081] The third step is to intercept the end surface area prod_reg according to the inner circle area and image_b to obtain image_b_reduced, such as Figure 7 As shown, image_b_reduced is then subjected to mean filtering using a 15×15 rectangular structural element to obtain image_mean, and the dark region reg in image_mean is extracted using automatic threshold segmentation, and a region reg_selected whose area is larger than a given first area threshold (such as 300) is selected, and then the region reg_selected is subjected to a region closing operation using a 15×15 rectangular structural element to obtain reg_closing, and then reg_closing is region filled to obtain reg_filled, and then a region reg_filled whose area is larger than the first area threshold is selected. The inner circle area is subtracted from the inner circle area by the second area threshold (such as 5000) to obtain reg_selected1, and then the candidate defect area is obtained. Finally, it is determined from the candidate defect area whether there is an area greater than the third area threshold (such as 8000). If so, the area greater than the third area threshold is determined to be the tab inward-turned area, and the lithium battery cell is output to have a tab inward-turned defect. Otherwise, the lithium battery cell is output to have no tab inward-turned defect. The processes of tab outward-turned defect detection and tab inward-turned defect detection are basically the same, with only individual preset data being different. It can be set according to the characteristics of the tab outward-turned defect, which will not be elaborated here.

[0082] The fourth step is to determine whether there is a blackening defect in the tab. First, use the image_diff image and the outer circle area to detect the dark area and obtain the corresponding image of the dark area. The detection method is: use a rectangular structural element 17×17 to perform median filtering, obtain the mean and variance deviation of the image corresponding to the dark area, calculate the segmentation threshold according to the mean and variance deviation, such as high_thre=mean-5*deviation, high_thre is the segmentation threshold, then, the area with a gray value in [1, high_thre] in the image corresponding to the dark area is segmented out, and a circular structural element with a radius of 8.5 is used to perform an open operation to filter out interference. Finally, it is determined from the area with a gray value in [1, high_thre] whether there is an area greater than the fourth area threshold (such as 1500). If so, the area greater than the fourth area threshold is determined to be a blackened area of ​​the outer ring of the pole ear, and the output lithium battery cell has a blackened outer ring defect of the pole ear, otherwise, the output lithium battery cell does not have a blackened outer ring defect of the pole ear. The process of detecting the blackened outer ring defect of the pole ear and the blackened inner ring defect of the pole ear is basically the same, and only individual preset data are different. It can be set according to the characteristics of the blackened inner ring defect of the pole ear, which will not be repeated here.

[0083] Assuming that it is determined that there is no serious end surface defect on the lithium battery cell, the lithium battery cell is subjected to the next step of center hole defect detection based on the end surface positioning image, as follows:

[0084] First, according to the end surface area prod_reg, the center coordinates of the area are obtained. According to the center coordinates and the preset center hole radius, the local area containing the center hole is extracted, and the center hole image is cut out on image_diff, as shown in Figure 8 As shown;

[0085] Then, the center hole image is enhanced and segmented using a fixed threshold with a grayscale range of [0,70]. Then, a 20×20 rectangular area is used for opening, and the area with the largest area is selected as the center hole area reg_hole, as shown in Fig. 9 As shown, the area within the green circle is the central hole area reg_hole;

[0086] Finally, the regional characteristic values ​​of the center hole area reg_hole are calculated, including the center hole area_hole, the maximum inscribed circle radius of the center hole inner_radius_hole, and the minimum circumscribed circle radius of the center hole outer_radius_hole. According to inner_radius_hole and outer_radius_hole, the normalized parameter radius_normal of the inner and outer circles is calculated. Radius_normal is calculated by the following formula:

[0087] radius_normal=abs(outer_radius_hole-inner_radius_hole) / inner_radius_hole;

[0088] Assuming that radius_normal is less than a given parameter threshold, the output lithium battery cell does not have a center hole defect; otherwise, the output lithium battery cell has a center hole defect.

[0089] In some embodiments, the above-mentioned lithium battery end surface defect detection method may further include steps S501 to S502:

[0090] Step S501, classifying the lithium batteries to be inspected according to the defect detection results, and determining the target defect detection classification corresponding to the lithium batteries to be inspected;

[0091] Step S502, determining a corresponding lithium battery processing solution according to the target defect detection classification, and processing the lithium battery to be detected according to the lithium battery processing solution.

[0092] In some embodiments, optionally, assuming that the defect detection result is that the lithium battery cell has a serious end face defect or a center hole defect, the lithium battery to be tested is determined to be an unqualified lithium battery and is discarded or sent back to the factory for remanufacturing. If the defect detection result is that the lithium battery cell does not have a serious end face defect and the lithium battery cell does not have a center hole defect, the lithium battery to be tested is determined to be a qualified lithium battery and is put into storage, packaged or processed in the next step.

[0093] Reference Fig.10 , Fig.10 : is an optional structural schematic diagram of a lithium battery end face defect detection device provided in an embodiment of the present application, the device is used to implement the above-mentioned lithium battery end face defect detection method, and the device may include:

[0094] The first module is used to obtain a lithium battery end face image corresponding to the lithium battery to be tested;

[0095] The second module is used to perform defect detection on the end face image of the lithium battery according to a preset defect detection type and determine the corresponding defect detection result.

[0096] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0097] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned lithium battery end face defect detection method when executing the computer program. The electronic device can be any smart terminal including a tablet computer.

[0098] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0099] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0100] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0101] The memory 902 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). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the lithium battery end face defect detection method of the embodiment of this application;

[0102] Input / output interface 903, used to implement information input and output;

[0103] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0104] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0105] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0106] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned lithium battery end face defect detection method is implemented.

[0107] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0108] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The embodiments of the present application provide a lithium battery end face defect detection method, device, electronic device and medium, which can realize automated lithium battery end face defect detection, improve the efficiency, stability and accuracy of lithium battery end face defect detection, and meet the high-speed and high-accuracy detection requirements of modern industry.

[0110] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0111] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0112] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0114] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0115] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0117] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in an assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed ASIC for this purpose.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0121] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for detecting end surface defects of a lithium battery, characterized in that: The method comprises the following steps: Acquire a lithium battery end face image corresponding to the lithium battery to be tested; According to the preset defect detection type, defect detection is performed on the lithium battery end face image to determine the corresponding defect detection result.

2. The lithium battery end surface defect detection method according to claim 1, characterized in that: The step of obtaining the lithium battery end face image corresponding to the lithium battery to be detected specifically includes: Acquire a positive terminal surface image and a negative terminal surface image of the battery cell of the lithium battery to be tested; The battery cell positive terminal face image and the battery cell negative terminal face image are subjected to image preprocessing to obtain a corresponding first end face image and a second end face image, wherein the first end face image corresponds to the battery cell positive terminal face image, and the second end face image corresponds to the battery cell negative terminal face image.

3. The lithium battery end surface defect detection method according to claim 2, characterized in that: The defect detection type includes multiple defect detection subcategories. The defect detection is performed on the lithium battery end face image according to the preset defect detection type to determine the corresponding defect detection result, specifically including: Obtaining the defect detection strategy corresponding to each of the defect detection subclasses; According to each of the defect detection strategies, defect detection is performed on the first end surface image to determine a first defect detection result corresponding to each of the defect detection subclasses; According to each of the defect detection strategies, defect detection is performed on the second end surface image to determine a second defect detection result corresponding to each of the defect detection subclasses; The defect detection result is determined according to the first defect detection result and the second defect detection result corresponding to each of the defect detection subclasses.

4. The lithium battery end surface defect detection method according to claim 1, characterized in that: The defect detection types include at least tab eversion defect detection, end face center hole defect detection, foreign object obstruction defect detection, tab inversion defect detection, tab blackening defect detection and tab deformation defect detection.

5. The lithium battery end face defect detection method according to claim 3, characterized in that: The performing defect detection on the first end face image according to each of the defect detection strategies to determine the first defect detection result corresponding to each of the defect detection subclasses specifically includes: Performing end face area positioning on the first end face image, and extracting a corresponding end face positioning image from the first end face image; According to each of the defect detection strategies, defect detection is performed on the end face positioning image to determine the first defect detection result corresponding to each of the defect detection subclasses.

6. The lithium battery end surface defect detection method according to claim 1, characterized in that: The performing image preprocessing on the battery cell positive end surface image and the battery cell negative end surface image to obtain the corresponding first end surface image and second end surface image specifically includes: The battery cell positive end surface image and the battery cell negative end surface image are sequentially subjected to image grayscale processing, image background filtering, image filtering and enhancement to generate the corresponding first end surface image and second end surface image.

7. The lithium battery end face defect detection method according to claim 1, characterized in that: The method further comprises: Classifying the lithium battery to be detected according to the defect detection result, and determining a target defect detection classification corresponding to the lithium battery to be detected; According to the target defect detection classification, a corresponding lithium battery processing scheme is determined, and the lithium battery to be detected is processed according to the lithium battery processing scheme.

8. A lithium battery end surface defect detection device, characterized in that: The device comprises: The first module is used to obtain a lithium battery end face image corresponding to the lithium battery to be tested; The second module is used to perform defect detection on the lithium battery end face image according to a preset defect detection type and determine the corresponding defect detection result.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the lithium battery end face defect detection method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the lithium battery end face defect detection method according to any one of claims 1 to 7 is implemented.

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