Battery top cover defect detection system and method based on image analysis

Abstract

The invention discloses a battery top cover defect detection system and method based on image analysis, and relates to the field of battery top cover defect detection, and the system comprises a mechanical arm module which is used for continuously grabbing a to-be-detected battery top cover; the rotating module is used for carrying the mechanical arm module to rotate at a preset speed; the acquisition module is used for acquiring battery top cover image data in a rotating state and storing the battery top cover image data; the construction module is used for uploading the standard structure parameters of the battery top cover and constructing a standard three-dimensional model of the battery top cover based on the standard structure parameters of the battery top cover; according to the invention, a traditional single detection mode is abandoned, an innovative mode of combining multiple capturing with rotary acquisition is adopted, battery top cover images are acquired from different positions and angles, appearance information is acquired in an omnibearing manner, and the detection accuracy and comprehensiveness are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery cover defect detection, and specifically provides a battery cover defect detection system and method based on image analysis. Background Art

[0002] The battery cover is an important component of the battery, located at the top of the battery, and plays roles of sealing, protection and connection. It is made of metal or composite materials, has insulation and anti-corrosion properties, can isolate external moisture, dust and other impurities, and prevent internal short circuit of the battery. At the same time, the battery cover integrates structures such as pole columns and safety valves, which is convenient for power transmission and internal pressure release, and is of great significance for ensuring the safe and stable operation of the battery.

[0003] The invention patent application with the publication number of 202210778944.6 discloses a battery cover defect detection method, including: obtaining images of the battery cover to be measured in partitions to obtain images corresponding to different regions of the battery cover to be measured; inputting the images corresponding to different regions into the corresponding defect detection composite model simultaneously to perform parallel detection on the images corresponding to different regions, and obtaining defect detection sub-results corresponding to each region; determining the defect detection result of the battery cover to be measured according to the defect detection sub-results corresponding to each region. This application aims to solve the problem that "However, there are various types of power batteries, with different sizes and rapid updates. During the detection of cover welding defects, due to the large difference in the length of battery covers, complete and clear image data cannot be collected, resulting in the inability of the prior art to be compatible with the cover detection of different power batteries".

[0004] However, in the application process of the existing technology for detecting battery cover defects through vision, it often cannot escape the essence that the collected battery cover images are from the static body of the battery cover, resulting in obstacles to the optimization and advancement of the detection accuracy of this detection technology.

[0005] Therefore, a battery cover defect detection system and method based on image analysis are proposed. Summary of the Invention

[0006] Aiming at the above-mentioned drawbacks of the prior art, the present invention provides a battery cover defect detection system and method based on image analysis, which can effectively solve the problems of the prior art.

[0007] To achieve the above purposes, the present invention is realized through the following technical solutions;

[0008] The present invention discloses a battery top cover defect detection system based on image analysis, including: a robotic arm module for continuously grasping the battery top cover to be detected; a rotation module for carrying the robotic arm module to rotate at a predetermined speed; an acquisition module for acquiring the image data of the battery top cover in the rotating state and storing the image data of the battery top cover; a construction module for uploading the standard structure parameters of the battery top cover and constructing a standard three-dimensional model of the battery top cover based on the standard structure parameters of the battery top cover; a picking module for receiving the standard three-dimensional model of the battery top cover constructed in the construction module and picking a reference image in the standard three-dimensional model of the battery top cover; a comparison module for comparing the image data of the battery top cover stored in the acquisition module with the reference image picked by the picking module to identify the differences between the two groups of images; a determination module for setting a defect determination threshold, synchronously receiving the difference recognition result in the comparison module, and determining whether there is a defect in the battery top cover from which the difference recognition result is derived based on the comparison between the difference recognition result and the defect determination threshold

[0009] Further, an electric suction cup is arranged at the grasping end of the robotic arm module. The robotic arm module continuously grasps the battery top cover at different positions on the surface of the battery top cover through the electric suction cup. After each grasping of the battery top cover, the rotation module carries the robotic arm module to rotate once at a predetermined speed, and the duration of a single rotation is greater than the time required for the acquisition module to run once;

[0010] Among them, the number of times the robotic arm module grasps the same battery top cover to be detected is not less than three, that is, the rotation module and the acquisition module synchronously run the robotic arm module the same number of times. The acquisition module is integrated by camera equipment, and the shutter speed of the camera equipment is lower than the rotation speed of the rotation module carrying the robotic arm module, that is, the time required for the camera equipment to open the shutter once is greater than the time required for the rotation module to carry the robotic arm module to rotate one week.

[0011] Further, before being put into use, both the robotic arm module and the rotation module are painted with a color specified to be different from the color of the battery top cover;

[0012] A segmentation unit and a marking unit are arranged at the lower level of the acquisition module. The segmentation unit is used to receive the image data of the battery top cover in the rotating state acquired by the acquisition module and segment the image of the battery top cover area in the image data of the battery top cover. The marking unit is used to obtain the attitude information of the source of the image data of the battery top cover in each rotating state and the attitude information when the acquisition module acquires the image data, and mark the two groups of attitude information on the corresponding battery top cover area image obtained by the segmentation unit during operation, and store the battery top cover area image with marks as the storage target of the acquisition module;

[0013] Among them, the image data of the battery top cover in the rotating state consists of the battery top cover area image and the background image. The background image comes from the robotic arm module and the rotating module. When the segmentation unit performs segmentation processing on the image data of the battery top cover in the rotating state, it distinguishes the battery top cover area image in the image data based on the painting colors of the robotic arm module and the rotating module.

[0014] Furthermore, the pose information of the source of the image data of the battery top cover in the rotating state is as follows:

[0015] Take any three or more corner points on the surface of the battery top cover as reference points, and obtain the position information of each reference point based on the distance from itself to each corner point measured by the deployed ranging device and its own position information, which is denoted as pose information;

[0016] The pose information when the acquisition module acquires image data is as follows:

[0017] The position information and angle of the imaging end of the camera device;

[0018] Among them, both the position information and the angle of the imaging end of the camera device are referenced based on the position information of the reference points of the battery top cover.

[0019] Furthermore, when the picking module picks up the reference image in the standard three-dimensional model of the battery top cover, it synchronizes with the pose information marked by traversing the battery top cover area image in the acquisition module, and intercepts the image of the battery top cover standard three-dimensional model in the same pose and rotating state in the three-dimensional space where the battery top cover standard three-dimensional model constructed by the construction module is located, that is, the reference image;

[0020] After the picking module picks up the reference image, it jumps to the acquisition module to run again. The acquisition module runs again to acquire the static image of the battery top cover, and the picking module further picks up the static reference image with the same viewing angle on the battery top cover standard three-dimensional model;

[0021] Among them, the number of acquisitions and pickups of the static image of the battery top cover and the static reference image is not less than 1, and is user-defined by the system end user, and it follows that the higher the accuracy requirement for battery top cover defect detection, the more the acquisition quantity, and vice versa, the less the acquisition quantity.

[0022] Furthermore, when the comparison module performs the comparison operation, the battery top cover image data stored in the acquisition module used is the battery top cover area image. The image difference recognition logic in the comparison module is expressed as:

[0023] ;

[0024] In the formula: is the image difference; is the total amount of the combination of the battery top cover area image and the corresponding reference image; is the total of the static image of the battery top cover and the corresponding static reference image; is the similarity between the image of the i-th group of battery top cover areas and the corresponding reference image; is the similarity between the static image of the j-th group of battery top covers and the corresponding static reference image; , is the weight;

[0025] wherein, represents taking the average of ; similarly, the larger is, the greater the difference between the images, that is, the greater the difference between the actual structural parameters of the battery top cover and the standard structural parameters of the battery top cover. and are both greater than 0, and the weight takes values such that the more corner points on the surface of the battery top cover, the smaller the value of the weight , and vice versa, the larger the value of the weight .

[0026] Furthermore, the similarity calculation logic of the two images is expressed as:

[0027] Taking as an example:

[0028] ;

[0029] In the formula: is the diameter of the image of the i-th group of battery top cover areas and the corresponding reference image; is the adjustment exponent. When , =-1, and vice versa, =1;

[0030] Taking as an example:

[0031] Feature extraction is performed on the static image of the j-th group of battery top covers and the corresponding static reference image, and it is transformed into a feature vector containing geometric features, including: side length, angle, area, and topological features, including: node connection relationship, number of loops and , and the vector dimension is x; a weight vector is defined to represent the importance of each feature dimension and obeys ;

[0032] Then ;

[0033] In the formula: are respectively the feature vectors and the c-th component of represents the Euclidean distance between two components is the bandwidth parameter of the Gaussian kernel function, which is used to adjust the sensitivity of similarity calculation.

[0034] Furthermore, when the determination module determines that there is a defective battery top cover, it controls the robotic arm module to sort the battery top cover, so that the defective battery top covers determined based on system detection are collected separately from the non-defective battery top covers.

[0035] Furthermore, the robotic arm module and the rotating module are electrically connected interactively through a medium, the robotic arm module and the rotating module are interactively connected with an acquisition module through a wireless network, the lower level of the acquisition module is interactively connected with a segmentation unit and a marking unit through a wireless network, the acquisition module is interactively connected with a construction module through a wireless network, the construction module is interactively connected with a picking module and a comparison module through a wireless network, the comparison module is interactively connected with a determination module through a wireless network, and the comparison module is interactively connected with the acquisition module through a wireless network.

[0036] On the other hand, a method for detecting defects in a battery top cover based on image analysis includes the following steps:

[0037] Deploy a robotic arm module and a rotating module to grab and rotate the battery top cover to be detected to collect image data of the battery top cover in a rotating state; segment and obtain the battery top cover area image from the battery top cover image data; upload the standard structure parameters of the battery top cover, construct a standard 3D model of the battery top cover based on the standard structure parameters of the battery top cover, and intercept the battery top cover standard 3D model image in the same pose as the pose information of the battery top cover image data during acquisition on the battery top cover standard 3D model, denoted as the reference image; collect static images from the same perspective on the battery top cover and the battery top cover standard 3D model again, denoted as the battery top cover static image and the static reference image respectively; combine the difference comparison between the battery top cover area image and the reference image and the difference comparison between the battery top cover static image and the static reference image to determine whether there are defects in the battery top cover; sort the battery top cover according to the determination result of whether there are defects in the battery top cover.

[0038] Adopting the technical solution provided by the present invention, compared with the known prior art, it has the following beneficial effects:

[0039] The present invention provides a system and method for detecting defects in a battery top cover based on image analysis. During the execution of the system and method, the traditional single detection mode is abandoned, and an innovative method of multiple grabs combined with rotational acquisition is adopted to collect images of the battery top cover from different positions and angles, obtaining appearance information comprehensively, and greatly improving the detection accuracy and comprehensiveness;

[0040] Meanwhile, a three-dimensional model is constructed based on standard structural parameters and compared with a reference image picked up to provide an accurate reference for detection. It can intercept images in corresponding poses in three-dimensional space and quickly and accurately identify the structural defects of the battery top cover with complex shapes.

[0041] In addition, a weight mechanism is introduced in image difference calculation and defect determination. The weight is flexibly adjusted according to the number of corner points on the surface of the battery top cover, significantly enhancing the intelligence and adaptability of detection, effectively reducing the misjudgment rate, meeting the detection requirements of different models of battery top covers, building a solid technical defense line for battery production quality control, and improving the detection accuracy. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a schematic structural diagram of a battery top cover defect detection system based on image analysis;

[0044] Figure 2 It is a schematic flowchart of a battery top cover defect detection method based on image analysis. Detailed Embodiments

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0046] The following further describes the present invention with reference to the embodiments.

[0047] Embodiment 1:

[0048] A battery top cover defect detection system based on image analysis in this embodiment, as Figure 1 shown, includes:

[0049] A robotic arm module for continuously grasping the battery top cover to be detected;

[0050] The grasping end of the robotic arm module is provided with an electric suction cup. The robotic arm module continuously grasps the battery top cover at different positions on the surface of the battery top cover through the electric suction cup. After each grasping of the battery top cover, the rotation module drives the robotic arm module to rotate once at a predetermined speed, and the duration of a single rotation is greater than the time required for the acquisition module to operate once;

[0051] Among them, the number of times the robotic arm module grasps the same battery top cover to be detected is not less than three times, that is, the rotation module and the acquisition module synchronize the robotic arm module to run the same number of times. The acquisition module is integrated by a camera device, and the shutter speed of the camera device is lower than the speed at which the rotation module drives the robotic arm module to rotate, that is, the time required for the camera device to open the shutter once is greater than the time required for the rotation module to drive the robotic arm module to rotate one week;

[0052] The rotation module is used to drive the robotic arm module to rotate at a predetermined speed;

[0053] The acquisition module is used to acquire the image data of the battery top cover in the rotating state and store the image data of the battery top cover;

[0054] Before the robotic arm module and the rotation module are put into use, they are both painted with a color specified to be different from the color of the battery top cover;

[0055] A segmentation unit and a marking unit are arranged at the lower level of the acquisition module. The segmentation unit is used to receive the image data of the battery top cover in the rotating state acquired by the acquisition module and segment out the battery top cover area image from the image data of the battery top cover. The marking unit is used to obtain the attitude information of the source of the image data of the battery top cover in each rotating state and the attitude information when the acquisition module acquires the image data, and mark the two sets of attitude information on the corresponding battery top cover area image obtained by the segmentation unit during operation, and store the battery top cover area image with marks as the storage target of the acquisition module;

[0056] Among them, the image data of the battery top cover in the rotating state consists of the battery top cover area image and the background image. The background image comes from the robotic arm module and the rotation module. When the segmentation unit performs segmentation processing on the image data of the battery top cover in the rotating state, it distinguishes the battery top cover area image from the image data based on the painting colors of the robotic arm module and the rotation module;

[0057] The attitude information of the source of the image data of the battery top cover in the rotating state is:

[0058] Any three or more corner points on the surface of the battery top cover are used as reference points, and the position information of each reference point is obtained based on the distance from the deployed ranging device to each corner point and its own position information, which is recorded as the attitude information;

[0059] The attitude information when the acquisition module acquires the image data is:

[0060] Position information and angle of the camera end of the camera device;

[0061] Among them, both the position information and the angle of the camera end of the camera device are referenced by the position information of the reference point on the battery top cover;

[0062] A construction module for uploading the standard structure parameters of the battery top cover and constructing a standard 3D model of the battery top cover based on the standard structure parameters of the battery top cover;

[0063] A picking module for receiving the standard 3D model of the battery top cover constructed in the construction module and picking a reference image in the standard 3D model of the battery top cover;

[0064] When the picking module picks a reference image in the standard 3D model of the battery top cover, it synchronizes with the attitude information of traversing the image markers in the battery top cover area in the acquisition module, and intercepts the standard 3D model image of the battery top cover in the same attitude and rotation state in the three-dimensional space where the standard 3D model of the battery top cover constructed by the construction module is located, that is, the reference image;

[0065] After the picking module picks the reference image, it jumps to the acquisition module to run again. The acquisition module runs again to acquire the static image of the battery top cover, and the picking module further picks the static reference image with the same viewing angle on the standard 3D model of the battery top cover;

[0066] Among them, the number of acquisitions and pickups of the static image of the battery top cover and the static reference image is not less than 1, and is user-defined by the system end user, and obeys that the higher the accuracy requirement for battery top cover defect detection, the more the acquisition quantity, and vice versa, the less the acquisition quantity;

[0067] A comparison module for comparing the battery top cover image data stored in the acquisition module with the reference image picked up by the picking module and identifying the differences between the two groups of images;

[0068] When the comparison module performs the comparison operation, the battery top cover image data stored in the acquisition module used is the battery top cover area image. The image difference identification logic in the comparison module is expressed as:

[0069] ;

[0070] In the formula: is the image difference; is the total amount of the combination of the battery top cover area image and the corresponding reference image; is the total amount of the combination of the static image of the battery top cover and the corresponding static reference image; is the similarity of the i-th group of battery top cover area images and the corresponding reference images; is the similarity of the j-th group of static images of the battery top cover and the corresponding static reference images; 、 is the weight;

[0071] Among them, means taking the average of ; similarly, similarly, the larger the , the greater the image difference, that is, the greater the difference between the actual structural parameters of the battery top cover and the standard structural parameters of the battery top cover. and are both greater than 0, and the weight takes values subject to the rule that the more corner points on the surface of the battery top cover, the smaller the value of the weight ; conversely, the larger the value of the weight ;

[0072] The similarity calculation logic of the two images is expressed as:

[0073] Taking as an example in

[0074] ;

[0075] In the formula: is the diameter of the i-th group of battery top cover area images and the corresponding reference image; is the adjustment index. When , = -1, conversely, = 1;

[0076] Taking as an example in

[0077] Feature extraction is performed on the j-th group of battery top cover static images and the corresponding static reference images, and they are converted into feature vectors containing geometric features, including: side length, angle, area, and topological features, including: node connection relationship, number of loops and , and the vector dimension is x; define the weight vector , which is used to represent the importance of each feature dimension and is subject to ;

[0078] Then ;

[0079] In the formula: are the c-th components of the feature vectors and respectively, represents the Euclidean distance between the two components, is the bandwidth parameter of the Gaussian kernel function, which is used to adjust the sensitivity of the similarity calculation;

[0080] ​​Through the above logical formula calculation, the calculation logic of image difference is defined to provide necessary operation data support for the determination module of the system in this embodiment;

[0081] When the determination module determines that there is a defective battery top cover, it controls the robotic arm module to sort the battery top cover, so as to separately collect the battery top covers determined to be defective and those without defects based on the system detection;

[0082] The determination module is used to set a defect determination threshold, synchronously receive the difference recognition result in the comparison module, and determine whether there is a defect in the battery top cover from which the difference recognition result comes based on the comparison between the difference recognition result and the defect determination threshold;

[0083] The robotic arm module and the rotating module are electrically interactively connected through a medium. The robotic arm module and the rotating module are interactively connected with a collection module through a wireless network. The lower level of the collection module is interactively connected with a segmentation unit and a marking unit through a wireless network. The collection module is interactively connected with a construction module through a wireless network. The construction module is interactively connected with a picking module and a comparison module through a wireless network. The comparison module is interactively connected with the determination module through a wireless network. The comparison module is interactively connected with the collection module through a wireless network.

[0084] In this embodiment, the robotic arm module operates to continuously grab the battery top cover to be detected. The rotating module synchronously drives the robotic arm module to rotate at a predetermined speed. The collection module runs later to collect the image data of the battery top cover in the rotating state and stores the image data of the battery top cover. The segmentation unit synchronously receives the image data of the battery top cover in the rotating state collected by the collection module and segments the battery top cover area image from the image data of the battery top cover. The marking unit real-time obtains the pose information of the image data source of each battery top cover in the rotating state and the pose information when the collection module collects the image data, and marks the two sets of pose information on the corresponding battery top cover area image segmented by the segmentation unit. The battery top cover area image with marks is stored as the storage target of the collection module. Then, the construction module uploads the standard structure parameters of the battery top cover and constructs a standard three-dimensional model of the battery top cover based on the standard structure parameters of the battery top cover. The picking module further receives the standard three-dimensional model of the battery top cover constructed by the construction module, picks up a reference image in the standard three-dimensional model of the battery top cover, and compares the image data of the battery top cover stored in the collection module with the reference image picked up by the picking module through the comparison module to identify the difference between the two sets of images. Finally, the determination module sets a defect determination threshold, synchronously receives the difference recognition result in the comparison module, and determines whether there is a defect in the battery top cover from which the difference recognition result comes based on the comparison between the difference recognition result and the defect determination threshold.

[0085] Through the operation of the system in the above embodiments, to a certain extent, it gets rid of the static image acquisition and detection of the battery top cover in the prior art, and further improves the accuracy of defect detection of the battery top cover based on vision detection technology.

[0086] Embodiment 2:

[0087] At the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 2 to further specifically describe a battery top cover defect detection system based on image analysis in Embodiment 1:

[0088] A battery top cover defect detection method based on image analysis includes the following steps:

[0089] Step 1: Deploy a robotic arm module and a rotating module to grab and rotate the battery top cover to be detected, so as to collect image data of the battery top cover in a rotating state;

[0090] Step 2: Segment and obtain the battery top cover area image from the battery top cover image data;

[0091] Step 3: Upload the standard structure parameters of the battery top cover, construct a standard 3D model of the battery top cover based on the standard structure parameters of the battery top cover, and intercept the standard 3D model image of the battery top cover in the same pose on the standard 3D model of the battery top cover according to the pose information when the battery top cover image data is collected, denoted as the reference image;

[0092] Step 4: Collect static images from the same perspective on the battery top cover and the standard 3D model of the battery top cover again, denoted as the battery top cover static image and the static reference image respectively;

[0093] Step 5: Combine the difference comparison between the battery top cover area image and the reference image and the difference comparison between the battery top cover static image and the static reference image to determine whether there are defects in the battery top cover;

[0094] Step 6: Sort the battery top covers according to the determination result of whether there are defects in the battery top cover.

[0095] In summary, during the execution of the system and method in the above embodiments, the traditional single detection mode is abandoned, and an innovative method of multiple captures combined with rotational acquisition is adopted to collect battery top cover images from different positions and angles, obtaining appearance information in all directions, greatly improving the detection accuracy and comprehensiveness. At the same time, a three-dimensional model is constructed based on standard structural parameters and a reference image is picked up for comparison, providing an accurate reference for detection, and capable of intercepting corresponding attitude images in three-dimensional space to quickly and accurately identify the structural defects of battery top covers with complex shapes. In addition, a weight mechanism is introduced in the image difference calculation and defect determination, and the weight is flexibly adjusted according to the number of corner points on the surface of the battery top cover, significantly enhancing the intelligence and adaptability of the detection, effectively reducing the misjudgment rate, meeting the detection requirements of different models of battery top covers, building a solid technical defense line for the quality control of battery production, and improving the detection accuracy.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A battery top cover defect detection system based on image analysis, characterized in that, Including: A robotic arm module for continuously grasping the top cover of the battery to be detected; A rotation module for carrying the robotic arm module to rotate at a predetermined speed; An acquisition module for acquiring image data of the battery top cover in a rotating state and storing the image data of the battery top cover; A construction module for uploading the standard structure parameters of the battery top cover and constructing a standard 3D model of the battery top cover based on the standard structure parameters of the battery top cover; A picking module for receiving the standard 3D model of the battery top cover constructed in the construction module and picking a reference image in the standard 3D model of the battery top cover; A comparison module for comparing the image data of the battery top cover stored in the acquisition module with the reference image picked by the picking module and identifying the differences between the two groups of images; A determination module for setting a defect determination threshold, synchronously receiving the difference identification result in the comparison module, and determining whether there is a defect in the battery top cover from which the difference identification result is derived based on the comparison between the difference identification result and the defect determination threshold.

2. The battery top cover defect detection system based on image analysis according to claim 1, wherein An electric suction cup is arranged at the grasping end of the robotic arm module. The robotic arm module continuously grasps the battery top cover at different positions on the surface of the battery top cover through the electric suction cup. After each grasping of the battery top cover, the rotation module carries the robotic arm module to rotate once at a predetermined speed, and the duration of a single rotation is greater than the time required for the acquisition module to run once; Among them, the number of times the robotic arm module grasps the same battery top cover to be detected is not less than three, that is, the rotation module and the acquisition module synchronously run the robotic arm module the same number of times. The acquisition module is integrated by camera equipment, and the shutter speed of the camera equipment is lower than the speed at which the rotation module carries the robotic arm module to rotate, that is, the time required for the camera equipment to open the shutter once is greater than the time required for the rotation module to carry the robotic arm module to rotate one week.

3. A battery top cover defect detection system based on image analysis according to claim 1, characterized in that, Before being put into use, both the robotic arm module and the rotation module are painted with colors specified to be different from the color of the battery top cover; A segmentation unit and a marking unit are arranged at the lower level of the acquisition module. The segmentation unit is used to receive the image data of the battery top cover in a rotating state acquired by the acquisition module and segment the image of the battery top cover area in the image data of the battery top cover. The marking unit is used to obtain the attitude information of the source of the image data of the battery top cover in each rotating state and the attitude information when the acquisition module acquires the image data, and mark the two groups of attitude information on the corresponding battery top cover area image obtained by the operation of the segmentation unit, and store the battery top cover area image with marks as the storage target of the acquisition module; Among them, the image data of the battery top cover in a rotating state consists of the battery top cover area image and the background image. The background image comes from the robotic arm module and the rotation module. When the segmentation unit performs segmentation processing on the image data of the battery top cover in a rotating state, the battery top cover area image is distinguished in the image data based on the painting colors of the robotic arm module and the rotation module.

4. The battery top cover defect detection system based on image analysis according to claim 3, wherein, The attitude information of the source of the image data of the battery top cover in a rotating state is: Taking any three or more corner points on the surface of the battery top cover as reference points, and obtaining the position information of each reference point based on the distance from itself to each corner point measured by the deployed ranging device and its own position information, which is recorded as the attitude information; The attitude information when the acquisition module acquires the image data is: Position information and angle of the camera device's imaging end; Among them, both the position information and the angle of the camera device's imaging end are referenced with respect to the position information of the reference point on the battery top cover.

5. The battery top cover defect detection system based on image analysis according to claim 1, characterized in that, When the picking module picks up the reference image in the standard 3D model of the battery top cover, it synchronizes with the attitude information of traversing the image markers in the battery top cover area in the acquisition module, and intercepts the image of the battery top cover standard 3D model in the same attitude and rotation state in the three-dimensional space where the battery top cover standard 3D model constructed by the construction module is located, that is, the reference image; After the picking module picks up the reference image, it jumps to the acquisition module to run again. The acquisition module runs again to acquire the static image of the battery top cover, and the picking module further picks up the static reference image with the same viewing angle on the battery top cover standard 3D model; Among them, the number of acquisitions and pickups of the battery top cover static image and the static reference image is not less than 1, and is user-defined by the system end user, and it obeys that the higher the accuracy requirement for battery top cover defect detection, the more the acquisition quantity, and vice versa, the less the acquisition quantity.

6. The battery top cover defect detection system based on image analysis according to claim 1, characterized in that, When the comparison module performs the comparison operation, the battery top cover image data stored in the acquisition module, that is, the battery top cover area image, is applied. The image difference recognition logic in the comparison module is expressed as: ； In the formula: is the image difference; is the total amount of the combination of the battery top cover area image and the corresponding reference image; is the total amount of the combination of the battery top cover static image and the corresponding static reference image; is the similarity between the i-th group of battery top cover area images and the corresponding reference images; is the similarity between the j-th group of battery top cover static images and the corresponding static reference images; , are weights; Among them, means to average For the same reason, the larger is, the greater the image difference is, that is, the greater the difference between the actual structural parameters of the battery top cover and the standard structural parameters of the battery top cover. Both and are greater than 0. The weight takes values subject to the rule that the more corner points on the surface of the battery top cover, the smaller the value of the weight is. Conversely, the larger the value of the weight 7. The battery top cover defect detection system based on image analysis according to claim 6, characterized in that, The similarity calculation logic of two images is expressed as: In the case of as an example: ； Wherein: is the diameter of the image of the top cover area of the i-th group of batteries and the corresponding reference image; is the adjustment index. When , =-1, otherwise, =1; In the case of as an example: Extract features from the static image of the top cover of the j-th group of batteries and the corresponding static reference image, and convert them into feature vectors containing geometric features, including side lengths, angles, areas, and topological features, including node connection relationships and the number of loops and , the vector dimension is x; define the weight vector , which is used to represent the importance of each feature dimension and follows ; Then ; Wherein: are the feature vectors respectively and the c-th component of, represents the Euclidean distance between the two components, is the bandwidth parameter of the Gaussian kernel function, used to adjust the sensitivity of similarity calculation.

8. The battery top cover defect detection system based on image analysis according to claim 7, characterized in that When the determination module determines a defective battery top cover, it controls the robotic arm module to sort the battery top cover, so as to separately collect the battery top covers determined to be defective and those without defects based on the system detection.

9. The battery top cover defect detection system based on image analysis according to claim 1, characterized in that, The robotic arm module and the rotation module are electrically interconnected through a medium. The robotic arm module and the rotation module are interconnected with the acquisition module through a wireless network. The lower level of the acquisition module is interconnected with a segmentation unit and a marking unit through a wireless network. The acquisition module is interconnected with the construction module through a wireless network. The construction module is interconnected with the picking module and the comparison module through a wireless network. The comparison module is interconnected with the determination module through a wireless network. The comparison module is interconnected with the acquisition module through a wireless network.

10. A method for detecting defects in a battery top cover based on image analysis, which is an implementation method of a system for detecting defects in a battery top cover based on image analysis according to any one of claims 1-9, characterized in that, Including the following steps: Step 1: Deploy the robotic arm module and the rotation module to grab and rotate the battery top cover to be detected to acquire the battery top cover image data in the rotation state; Step 2: Segment and obtain the battery top cover area image from the battery top cover image data; Step 3: Upload the standard structural parameters of the battery top cover, construct the standard 3D model of the battery top cover based on the standard structural parameters of the battery top cover, and intercept the image of the battery top cover standard 3D model in the same attitude on the battery top cover standard 3D model according to the attitude information when the battery top cover image data is acquired, denoted as the reference image; Step 4: Acquire static images from the same viewing angle on the battery top cover and the battery top cover standard 3D model again, denoted as the battery top cover static image and the static reference image respectively; Step 5: Combine the difference comparison between the battery top cover area image and the reference image and the difference comparison between the battery top cover static image and the static reference image to determine whether there are defects in the battery top cover; Step 6: Sort the battery top cover according to the determination result of whether there are defects in the battery top cover.

Images