WO2021248554A1 - 高速高精度锂离子电池极片的毛刺检测方法及检测系统 - Google Patents

高速高精度锂离子电池极片的毛刺检测方法及检测系统 Download PDF

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WO2021248554A1
WO2021248554A1 PCT/CN2020/097452 CN2020097452W WO2021248554A1 WO 2021248554 A1 WO2021248554 A1 WO 2021248554A1 CN 2020097452 W CN2020097452 W CN 2020097452W WO 2021248554 A1 WO2021248554 A1 WO 2021248554A1
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detection
speed
burr
light source
ion battery
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PCT/CN2020/097452
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English (en)
French (fr)
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周华民
刘家欢
黄天仓
杨志明
张云
黄志高
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深圳市信宇人科技股份有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0106—General arrangement of respective parts
    • G01N2021/0112—Apparatus in one mechanical, optical or electronic block
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques

Definitions

  • the invention belongs to the technical field of visual inspection, and specifically relates to a burr detection method and a matching detection system for a burr in a lithium ion battery pole piece slitting process.
  • Lithium-ion batteries are currently the most widely used power source in the world due to their large capacity and high energy efficiency ratio. As the performance requirements of lithium-ion batteries are getting higher and higher, the accuracy requirements for each process of battery manufacturing have also become higher.
  • the production process of the pole piece of the lithium ion battery is a key link, and the quality control is very strict. During the slitting process of lithium battery pole pieces, defects such as burrs will be generated on the current collector of the pole pieces. The burrs can pierce the battery diaphragm during battery assembly, cause the battery to be short-circuited and discarded, and even cause safety problems. Therefore, the detection of burr defects of lithium-ion battery pole pieces is very important.
  • the existing burr detection method simply uses an industrial camera to shoot the image of the pole piece, and then the burr detection is performed by the detection algorithm.
  • the existing detection technology cannot meet the production requirements. This requires a well-arranged light source structure to light the pole piece, and then design a suitable image acquisition system and image processing system to complete the high-speed and high-precision pole piece burr detection requirements.
  • the present invention provides a high-speed and high-precision burr detection method and detection system for lithium-ion battery pole pieces based on a modular image acquisition system composed of a herringbone structure light source and a high-speed line scan camera; It has the characteristics that it can meet the requirements of production under the condition of high-speed slitting and small burr size.
  • the herringbone structure light source of the present invention is a customized light source matched with the camera used in the present invention.
  • Two common surface light sources similar to strip light sources contact at one end to form a herringbone structure.
  • the light source and the camera form a modular image acquisition Module.
  • the angle of this herringbone structure can be adjusted to suit different shooting scenes.
  • a flat backlight is placed opposite to the chevron light source at the lower part of the edge portions on both sides of the pole piece of the lithium battery to improve the contrast of the pole piece.
  • the image acquisition device in the present invention is a high-speed line scan camera, and the installation direction is perpendicular to the movement direction of the pole piece.
  • the high-speed scanning of the line scan camera is used to match the high-speed movement of the pole piece during winding.
  • the line frame resolution of general line scan cameras can reach up to 16384 pixels, it can meet the requirements of high-precision burr detection.
  • the deep learning-based defect detection algorithm proposed by the present invention uses Convolutional Neural Network (CNN) to process input images, extract image features, and classify and locate defect regions.
  • CNN Convolutional Neural Network
  • the present invention also proposes a high-speed and high-precision lithium-ion battery pole piece burr visual inspection system, which includes mechanical structures such as cameras, light sources, industrial computers, and corresponding light source controllers, as well as matching detection algorithms. Among them:
  • the camera is a high-speed line scan camera, and the line frame resolution needs to be selected according to the specific use scene.
  • the width of the battery pole piece involved in the present invention is about 10-20cm after the slitting process, and the size of the burr is about 5 ⁇ m.
  • the defect area should be described by 3-5 pixels, so the pixels of the camera The resolution should reach 1pixel/ ⁇ m.
  • this system uses two above-mentioned cameras and light source modules to detect burr defects on both sides of the pole piece.
  • the FOV of the camera used in this system is 10mm. Therefore, a line scan camera with a line frame resolution of 12K is selected in this system.
  • the speed of the pole piece during rewinding is about 1m/s, so the sampling frequency of the camera should reach 106 lines/sec.
  • This system uses a scanning camera with a vertical resolution of 4 pixels and a sampling frequency of 300KHz. Therefore, the line scan camera used in this system has a resolution of 12K ⁇ 4 pixels and a frequency of 300KHz to meet the needs of high-speed and high-precision detection.
  • the said camera is equipped with an ordinary optical lens, and a lens matched with the working distance of the camera is adopted in this system;
  • the said herringbone light source is a pure-color strip-shaped surface light source. White light is used in this system.
  • the light source controller used in conjunction with the light source can perform 256-level brightness dimming with the light source.
  • the plane backlight source is a pure-color surface light source, and white light is used in this system.
  • the light source controller used in conjunction with the light source can perform 256-level brightness dimming with the light source.
  • the burr detection system also includes a mechanical structure part, specifically a fixed fixture of the image acquisition device, a light source angle adjustment mechanical structure, and a pole piece air-jet cleaning pretreatment mechanism;
  • the described burr detection algorithm uses a CNN-based defect detection framework, preprocesses the image output by the line scan camera and inputs it into the detection framework, performs target detection, and outputs the defect type and location.
  • the model in the algorithm can also be trained to detect defects such as scratches, inclusions, and leaking foils.
  • the described CNN-based defect detection framework requires offline training before actual use.
  • the specific method is as follows:
  • the model parameters are called by the matching detection system, and the defect area is predicted online during actual detection.
  • the described defect detection algorithm process is to obtain the image output by the line scan camera, first perform the image average filter processing on the image to reduce the effect of noise in the image on the result, and then use the gray linear change to improve the defect area and background area in the image Contrast to highlight defects for easy detection. Then, the processed image is scaled to a fixed size and then passed into the above-mentioned CNN model for defect classification and location prediction.
  • the detection method of the lithium battery pole piece burr detection system includes the following steps:
  • the light source and image acquisition system are installed on the pole piece winding assembly line, and the light source and camera are adjusted well to obtain a good pole piece sampling image, power on, and initialize the detection system;
  • the high-speed line scan camera performs high-frequency sampling on the pole pieces cleaned by the air jet, and calculates the corresponding sampling frequency according to the winding line speed, so that the camera sampling frequency is synchronized with the pole piece movement speed;
  • the detection system reads the line scan camera image from the memory at a predetermined time interval, and then preprocesses the complete image after stitching the scan camera line frame, uses the mean filter to smooth the noise of the input image, and then executes the linear gray scale transformation to highlight Defect area, and then input the preprocessed result image into the CNN defect detection model for burr detection, and complete the detection in a very short time;
  • the CNN defect detection model is modified according to the YOLO v3 framework. Because the content information in the industrial defect image is less, and the requirement of high-speed inspection is taken into consideration, the original YOLO v3 framework is streamlined and modified to adapt to the defect inspection task.
  • the CNN defect detection model includes a backbone feature extraction network VGG16 model and three additional convolution branches for predicting results.
  • the result of the backbone network VGG16 contains 5 consecutive building blocks consisting of a convolutional layer, a pooling layer and an activation function. These 5 building blocks have a total of 13 layers, which are used to extract features from the image.
  • the additional 3 predictive convolution branches are directly derived from the main VGG16 network to detect defects on multi-scale features.
  • the first predictive convolution branch is derived from the fifth building block of the VGG16 backbone network, and features are extracted through a 3 ⁇ 3 convolution layer, and then a 1 ⁇ 1 convolution layer predicts the result.
  • the features output by the fifth layer after being used to predict the results also pass through the first upsampling layer, are expanded by a factor of 2 and then merged with the features from the fourth building block, and then enter the second prediction convolution branch, after a The 3 ⁇ 3 convolutional layer extracts features, and then a 1 ⁇ 1 convolutional layer predicts the result.
  • the merged feature passes through the second up-sampling layer, and is expanded by a factor of 2 and then merged with the feature from the third building block. Then enter the third prediction convolution branch, extract features through a 3 ⁇ 3 convolution layer, and then predict the result by a 1 ⁇ 1 convolution layer. Predicting the results after three different scale features improves the model's detection accuracy of defects, especially for small defect types, such as leaking foils.
  • each predicted convolution branch predicts 3 different suspected defect region candidate frames at each pixel position of the feature map.
  • the first is the hardware part, including the adjustment of the camera's installation position and working distance to obtain a clear and complete pole piece edge image; at the same time, the brightness and angle of the herringbone light source are adjusted to cooperate with the camera to adjust to obtain a good sample image;
  • the second part is the system, including the image reading interval, camera parameter settings, and exposure parameters to coordinate with the high-speed winding pole piece movement to obtain high-quality sampled images.
  • the lithium battery pole piece burr detection method and system based on the high-speed line scan camera and the herringbone structure light source provided by the present invention mainly have the following beneficial effects :
  • the light source with herringbone structure can well light the surface of the lithium battery pole piece according to the sampling characteristics of the camera to obtain a clear image. It forms a light source pair with the flat backlight under the pole piece to make burr defects
  • the pole piece background is easy to distinguish;
  • the adopted high-speed line scan camera can cope with the scene of precise detection of tiny burrs in the process of high-speed slitting and winding of pole pieces.
  • the speed of s is as small as 5 ⁇ m for burr defect detection, and the burr detection accuracy and recall rate can reach more than 99%, so as to realize the high-speed and high-precision detection of lithium battery pole pieces.
  • the described image acquisition module has the advantage of modularity.
  • the hardware structure and the matching detection software system can flexibly adapt to the detection of pole piece burrs of different widths, which improves the efficiency and has better applicability and economy. .
  • Fig. 1 is a schematic diagram of an image acquisition module composed of a high-speed line scan camera and a herringbone light source according to the present invention
  • Fig. 2 is a schematic top view of the structure of Fig. 1;
  • FIG. 3 is a schematic diagram of the optical path structure of the image acquisition module described in FIG. 1;
  • FIG. 4 is a schematic diagram of the top view structure of the hardware layout of the detection system according to the present invention.
  • Figure 5 is a schematic diagram of the A-direction structure in Figure 4.
  • FIG. 6 is a schematic diagram of the block structure of the detection system according to the present invention.
  • FIG. 7 is a schematic diagram of the block structure of the detection algorithm according to the present invention.
  • FIG. 8 is a schematic diagram of the structure of the defect detection frame according to the present invention.
  • Fig. 9 is a schematic diagram of a screenshot of the detection result of the detection system used in the present invention.
  • Figures 1 to 5 disclose a high-speed and high-precision burr detection system for lithium-ion battery pole pieces, including an image acquisition module 1 and a detection module 2.
  • the image acquisition module 1 includes a light source 11 And a line scan camera 12, the light source 11 is used to illuminate a predetermined part of the pole piece, the line scan camera 12 scans and photographs the illuminated part of the pole piece, and transmits the captured image to the detection module 2.
  • the detection module 2 processes the captured images according to the following high-speed and high-precision burr detection method of lithium-ion battery pole pieces.
  • the light source 11 includes a first light source 111 and a second light source 112, the first light source 111 is arranged on the first mounting member 113; the second light source 112 is arranged on the second mounting member 114, the The upper ends of the first mounting member 113 and the second mounting member 114 are pivotally connected by a shaft 115 to form a herringbone light source.
  • the line scan camera 12 is arranged on the center line of the herringbone light source, and the line scan camera 12 The lens is aimed at the irradiation area of the light source 11.
  • a flat backlight 13 is further provided under the pole piece 15, and the light emitting direction of the flat backlight 13 faces the bottom surface of the pole piece; it is used to distinguish between burr defects and Pole piece background.
  • image acquisition modules 1 which are respectively located on both sides of the pole piece in the length direction, and are used to collect burrs on both sides of the pole piece.
  • the present invention further includes an air-jet cleaning device 14 which is located on the upstream side of the image acquisition module 1 and is used to remove dust from the pole pieces.
  • the burr detection system scheme of the present invention is that after the hardware installation and debugging of the system is completed, the supporting detection software completes the initialization of all components, and the industrial computer controls the herringbone structure light source to reach the predetermined brightness through the light source controller.
  • the machine is connected to multiple high-speed line scan cameras through the switch. After the image acquisition module is sampled, the software system calls the algorithm to complete the glitch detection, and feeds back the detection result to the execution end to complete the predetermined action.
  • the detection system software uses the PLC to control the air-jet cleaning device to remove impurities on the surface of the lithium battery pole pieces to prevent the impurities from affecting the subsequent burr detection;
  • the image acquisition module performs pole piece image sampling at a set frequency, acquires the pole piece surface image, and stores it in the memory for later use.
  • Two image acquisition modules distinguish and collect edge images on both sides of the pole piece;
  • the detection software system obtains the images obtained in step S31 at regular intervals, first stitches the images obtained by line scanning into a large image, and then merges the images on both sides obtained by the two image modules into one image, and then matches
  • the defect detection software system first performs filtering and noise reduction processing on the image, and then uses the image grayscale transformation algorithm to adjust the contrast of the pole image to highlight the defect area. After the preprocessed image is obtained, it is sent to the CNN detection framework to detect the burr in the image. At the same time, the image module is still synchronized to continue to collect images for backup;
  • the convolution kernel parameters of each layer of the CNN structure model used in the present invention are shown in the following table.
  • Conv stands for convolutional layer.
  • the CNN model uses the VGG16 structure as the backbone network, so in the offline training stage, the migration learning method is used to use the pre-trained model on the public image data set in the defect image training process of the present invention to improve the accuracy of model detection. , Reduce the training time and the number of training samples required.
  • Fig. 9 is a schematic diagram of a screenshot of the detection result of the detection system used in the present invention.

Abstract

一种高速高精度锂离子电池极片（15）的毛刺检测方法及检测系统，其中，方法包括S1、上电，初始化检测系统；S2、检测开始时，高速线扫描相机（12）对极片（15）进行高频率采样；S31、检测系统间隔预定时间从内存中读取线扫描相机（12）采集的图像；S32、在检测系统读取扫描图像并检测的同时，线扫描相机（12）同时并行地对极片（15）进行图像采集并存储到内存中，等待下一次读取；S4、根据极片（15）收卷线速度v和算法检测总耗时ta计算出极片（15）缺陷后处理缓冲区的长度，算法检测出毛刺缺陷后，按照要求进行暂停收卷或者标记处理；S5、重复S31至S4步骤，直至所有极片（15）检测完成。检测方法及检测系统在高速分切并且毛刺尺寸很小的情况下，都可以满足生产的要求。

Description

高速高精度锂离子电池极片的毛刺检测方法及检测系统 技术领域
本发明属于视觉检测技术领域，具体的涉及一种针对锂离子电池极片分切工艺中的毛刺检测方法及其配套的检测系统。
背景技术
锂离子电池由于容量大，能效比高，目前是世界上应用最为广泛的电源。由于锂离子电池的性能需求越来越高，对电池制造各工序的精度要求也随之变高。锂离子电池的极片生产工艺作为关键环节，质量把控十分严格。锂电池极片在分切的过程中，会在极片的集流体上产生毛刺等缺陷。毛刺在电池组装时会刺破电池隔膜，导致电池短路报废，甚至还会产生安全问题。因此，锂离子电池极片的毛刺缺陷检测至关重要。
现有的毛刺检测方法简单地采用工业相机拍摄极片的图像，然后由检测算法进行毛刺检测。但是由于工业相机的帧率和采样精度的限制，无法满足高速高精度的毛刺检测要求。在高速分切并且毛刺尺寸很小的情况下，现有的检测技术无法满足生产要求。这是需要一种良好布置的光源结构对极片打光，然后设计合适的图像采集系统和图像处理系统来完成高速高精度的极片毛刺检测需求。发明概述
技术问题
问题的解决方案
技术解决方案
针对上述所描述的难题，本发明提供了一种其于人字型结构光源和高速线扫描相机组成的模块化的图像采集系统的高速高精度锂离子电池极片的毛刺检测方法及检测系统；具有在高速分切并且毛刺尺寸很小的情况下，都可以满足生产的要求的特点。
本发明的人字型结构光源为与本发明使用的相机配套的定制光源，由两个类似条形光源的普通面光源一端接触形成人字型结构，该光源与相机组成一个模块 化的图像采集模组。此外，这种人字型结构的夹角可以调节以适应不同的拍摄场景。进一步地，为了提高图像采集质量，锂电池极片两侧边缘部分的下部与人字型光源对立地放置一个平面背光源，用于提高极片的反差。本发明中的图像采集装置是高速线扫描相机，安装方向与极片的运动方向垂直。利用线扫描相机的高速扫描来匹配极片收卷过程中的高速运动。同时由于一般线扫描相机的行帧分辨率最高可达16384pixel，可以满足高精度毛刺检测要求。
本发明提出的基于深度学习的缺陷检测算法是利用卷积神经网络(Convolution al Neural Network，简称CNN)处理输入图像，提取图像特征，对缺陷区域进行分类和定位。
为了实现上述的目的，本发明还提出了一种高速高精度锂离子电池极片毛刺视觉检测系统，其包括相机、光源、工控机以及相应光源控制器等机械结构以及配套的检测算法，其中：
所述的相机为高速线扫描相机，需要根据具体使用场景选用行帧分辨率。例如，本发明涉及的电池极片在分切工艺之后的宽度约为10-20cm，毛刺的尺寸约为5μm，为了保证检测精度，缺陷区域应该有3-5个像素来描述，因此相机的像素分辨率应该达到1pixel/μm。为了节约成本和提高效率，本系统采用两个上述的相机和光源模组，分别检测极片两侧边缘测毛刺缺陷。本系统选用相机的可视范围(FOV)为10mm。因此，本系统中选用行帧分辨率为12K的线扫描相机。极片在收卷时的速度约为1m/s，因此相机的采样频率应该达到10 6行/秒，本系统选用垂直分辨率为4像素，采样频率为300KHz的扫描相机。因此本系统采用的线扫描相机为分辨率12K×4像素，频率为300KHz，以满足高速高精度的检测需求。
所述的相机配备普通的光学镜头，在本系统中采用与相机工作距离配套的镜头；
所述的人字型光源为纯色条形面光源，在本系统中采用白光，配套使用的光源控制器可以与光源进行256级亮度调光。
所述的平面背光源为纯色面光源，在本系统中采用白光，配套使用的光源控制器可以与光源进行256级亮度调光。
所述的毛刺检测系统还包括机械结构部分，具体的有图像采集装置的固定夹具、光源角度调节机械结构和极片喷气清洁预处理机构；
所述的毛刺检测算法采用基于CNN的缺陷检测框架，将线扫描相机输出的图像进行预处理之后输入到检测框架中，进行目标检测，输出缺陷类型和位置。具体的，根据不同的使用环境和需求，该算法中的模型还可以训练来检测划伤、夹杂、漏箔等缺陷。
所述的基于CNN的缺陷检测框架在实际使用之前需要离线训练，具体的做法为：
(1)实现所述的基于CNN的缺陷检测模型框架；
(2)采集预定量(优选为200-300张)的含有缺陷的极片图像，由专业人员用矩形框标记出缺陷的位置，并给出缺陷的类别，得到训练数据样本；
(3)将训练数据样本输入CNN模型中进行特征提取计算，然后对图中的缺陷进行分类并回归计算出缺陷的位置，同时给出疑似区域为该类别缺陷的概率；
(4)将步骤(3)中的模型计算结果与实际人工标记的结果进行对比，计算出模型的计算误差，使用反向传播规则更新模型的参数，从而实现模型的优化。
在CNN模型框架离线训练完成后，模型参数由配套的检测系统负责调用，在实际检测时在线预测缺陷区域。
所述的缺陷检测算法流程为，获取线扫描相机输出的图像，先对图像进行图像均值滤波处理，降低图像中噪声对结果的影响，然后使用灰度线性变化，提高图像中缺陷区域和背景区域的对比度，以突出缺陷便于检测。再将处理后的图像缩放至固定的大小后传入上述CNN模型中进行缺陷类别分类和位置预测。
优选地，所述的锂电池极片毛刺检测系统的检测方法，包括如下步骤：
S1、光源和图像采集系统安装在极片收卷的流水线上，并将光源和相机调试好，获得良好的极片采样图像，上电，初始化检测系统；
S2、检测开始时，高速线扫描相机对喷气清理后的极片进行高频率采样，并根据收卷线速度计算对应的采样频率，使相机采样频率与极片运动速度同步；
S31、检测系统间隔预定时间从内存中读取线扫描相机图像，然后根据扫描相机行帧拼接后的完整图像进行预处理，使用均值滤波对输入图像进行平滑降噪 ，然后执行线性灰度变换突出缺陷区域，然后将预处理结果图像输入到CNN缺陷检测模型中，进行毛刺检测，在极短的时间内完成检测；
S32、在系统读取扫描图像并检测的同时，相机此时仍然并行地对极片进行图像采集并存储到内存中，等待下一次读取；
S4、根据极片收卷线速度v和所述检测算法总耗时t a很容易计算出极片缺陷后处理缓冲区的长度，长度为L c＝v*t a，所述缺陷检测算法在检测出毛刺缺陷后，按照要求进行暂停收卷或者标记处理。算法计算出毛刺的相对位置，在缓冲区内对毛刺进行处理；
S5、重复S31至S4步骤，直至所有极片检测完成。
优选地，所述的CNN缺陷检测模型是根据YOLO v3框架修改得到的。由于工业缺陷图像中的内容信息较少，同时考虑到高速检测的需求，因此，将原YOLO v3框架精简并适应所述的缺陷检测任务做出修改。具体来说，所述的CNN缺陷检测模型包括一个主干特征提取网络VGG16模型和三个额外的用于预测结果的卷积分支组成。主干网络VGG16结果包含5个连续的由卷积层、池化层和激活函数组成的构建模块。这5个构建块共有13个层，用于从图像中提取特征。为了提高模型对小缺陷区域的检测精度，同时减少计算时间，额外的3个预测卷积分支直接从主干的VGG16网络中引出，在多尺度特征上检测缺陷。具体来说，第一个预测卷积分支从VGG16主干网络的第5个构建块之后引出，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果。其中，第5层输出的特征在用于预测结果之后还经过第一上采样层，扩大2倍后与来自第4个构建块的特征进行融合，然后进入第二个预测卷积分支，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果。同样地，该融合后的特征经过第二上采样层，扩大2倍后与来自第3个构建块的特征进行融合。然后进入第三个预测卷积分支，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果。经过三个不同尺度的特征进行结果预测，提高了模型对缺陷的检测精度，尤其是对小的缺陷类型，例如漏箔等。
优选地，由于锂电池极片的缺陷的种类不多，变化较少，每个预测卷积分支在特征图的每个像素点位置预测出3个不同的疑似缺陷区域候选框。
为了保证成像质量和检测精度，需要对整个系统做初步的调试。首先是硬件部分，包括相机的安装位置和工作距离的调节，以能获得清晰完整的极片边缘图像为准；同时还有人字型光源的亮度及角度调节，配合相机调试获得良好的采样图像；其次是系统的部分，包括图像读取间隔时间，相机参数设置、曝光参数，以便与高速收卷的极片运动协调来获得高质量的采样图像。
发明的有益效果
有益效果
总体而言，通过本发明所构思的以上技术方案与现有技术相比，本发明提供的基于高速线扫描相机和人字型结构光源的锂电池极片毛刺检测方法及系统主要具有以下有益效果：
(1)人字型结构光源能根据相机的采样特点，对锂电池极片的表面进行良好的打光，从而获得清晰的图像，与极片下部的平面背光源形成光源对，使毛刺缺陷与极片背景容易区分；
(2)采用的高速线扫描相机能够应对极片高速分切和收卷过程中的微小毛刺精确检测的场景，本发明的技术方案可以应对所述的20cm宽度左右的锂电池极片在1m/s的速度收卷时小到5μm的毛刺缺陷检测，毛刺检测精确率和召回率均可以达到99％以上，从而实现锂电池极片的高速高精度检测。
(3)所述的图像采集模组具有模块化的优点，硬件结构和配套的检测软件系统可以很灵活地适应不同宽度的极片毛刺检测，提高了效率，具有较好的适用性及经济性。
对附图的简要说明
附图说明
图1是本发明所述高速线扫描相机与人字型光源组成的图像采集模组示意图；图2是图1的俯视结构示意图；
图3是图1所述的图像采集模组的光路结构示意图；
图4是本发明所述的检测系统硬件布局俯视结构示意图；
图5是图4中的A向结构示意图；
图6是本发明所述的检测系统方框结构示意图；
图7是本发明所述的检测算法方框结构示意图；
图8是本发明所述的缺陷检测框架结构示意图；
图9是本发明配套使用的检测系统检测结果截图示意图。
实施该发明的最佳实施例
本发明的最佳实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
请参见图1至图5，图1至图5揭示的是一种高速高精度锂离子电池极片的毛刺检测系统，包括图像采集模块1和检测模块2，所述图像采集模块1包括光源11和线扫描相机12，所述光源11用于给极片预定部位进行照明，所述线扫描相机12对极片被照明后的部位进线扫描拍照，并将所拍的图像传输线所述检测模块2。
优选的，所述检测模块2依据下述高速高精度锂离子电池极片的毛刺检测方法对所拍的图像进行处理。
优选的，所述光源11包括第一光源111和第二光源112，所述第一光源111设在第一安装件113上；所述第二光源112设在第二安装件114上，所述第一安装件113和第二安装件114的上端通过转轴115枢接，构成人字型光源，所述线扫描相机12设在人字型光源的中心线上，且所述线扫描相机12的镜头对准所述光源11的照射区域。
优选的，在远离所述光源11的一侧，极片15的下方还设有平面背光源13，所述平面背光源13的发光方向朝向所述极片的下底面；用于区分毛刺缺陷与极片背景。
优选的，所述图像采集模块1为两个，分别位于极片长度方向的两侧，用于采集极片两侧的毛刺。
优选的，本发明还包括喷气清洁装置14，所述喷气清洁装置14位于所述图像采集模块1的上游侧，用于对极片进行除尘。
结合图6所示，本发明的毛刺检测系统方案为，在系统的硬件安装调试完成后 ，配套检测软件完成所有部件的初始化，工控机通过光源控制器控制人字型结构光源达到预定亮度，工控机通过交换机连接多个高速线扫描相机，图像采集模块采样完成后，软件系统调用算法完成毛刺检测，并将检测结果反馈给执行端，完成预定的动作。
结合图7，以下阐明本发明的检测步骤：
S1、调试好光源位置、光源角度、相机安装位置、工作距离并设置好图像采集模组的参数。同时将喷气清洁装置和缓冲区的处理执行端安装到预定位置；
S2、检测开始时，检测系统软件通过PLC控制喷气清洁装置将锂电池极片表面杂质清除，防止杂质影响后续的毛刺检测；
S31、图像采集模组以设定的频率进行极片图像采样，获取极片表面图像，存入内存待用。两个图像采集模组分辨采集极片两侧的边缘图像；
S32、检测软件系统每隔一定的时间获取S31步骤中获取的图像，先按行将线扫描得到的图像拼接成大图，再将两个图像模组获取的两侧图像合并成一张图像，然后配套的缺陷检测软件系统先对图像进行滤波降噪处理，再使用图像灰度变换算法将极片图像进行对比度调节，突出缺陷区域，得到预处理图像之后送入CNN检测框架中检测图像中的毛刺。与此同时，图像模组仍然同步继续采集图像备用；
S4、由于检测算法需要一定的时间，所以此时上次采集图像并检测之后的区域已经进入设定的缓冲处理区，系统检测检测结果通过PLC反馈给执行端，执行预定的停机检查或者其他标记工作；
S5、重复S31至S4步骤，直至所有极片检测完成。
优选地，如图8所示，本发明使用的CNN结构模型各层的卷积核参数如下表所示。其中Conv表示卷积层convolutional layer的缩写。
所述的CNN模型采用VGG16结构作为主干网络，因此在离线训练阶段，采用迁移学习的方法将公开图像数据集上的预训练模型用于本发明的缺陷图像训练过程，用于提高模型检测准确率、降低训练时间和所需训练样本数。
Figure PCTCN2020097452-appb-000001
Figure PCTCN2020097452-appb-000002
图9是本发明配套使用的检测系统检测结果截图示意图。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种高速高精度锂离子电池极片的毛刺检测方法，包括如下步骤：
    S1、上电，初始化检测系统；
    S2、检测开始时，高速线扫描相机极片进行高频率采样，并根据收卷线速度计算对应的采样频率，使相机采样频率与极片运动速度同步；
    S31、检测系统间隔预定时间从内存中读取线扫描相机图像，然后根据扫描相机行帧拼接后的完整图像进行预处理，使用均值滤波对输入图像进行平滑降噪，然后执行线性灰度变换突出缺陷区域，然后将预处理结果图像输入到CNN缺陷检测模型中，进行毛刺检测，在极短的时间内完成检测；
    S32、在检测系统读取扫描图像并检测的同时，线扫描相机同时并行地对极片进行图像采集并存储到内存中，等待下一次读取；
    S4、根据极片收卷线速度v和所述检测算法总耗时ta计算出极片缺陷后处理缓冲区的长度，长度为Lc＝v*ta，所述缺陷检测算法在检测出毛刺缺陷后，按照要求进行暂停收卷或者标记处理；算法计算出毛刺的相对位置，在缓冲区内对毛刺进行处理；
    S5、重复S31至S4步骤，直至所有极片检测完成。
  2. 根据权利要求1所述的高速高精度锂离子电池极片的毛刺检测方法，其特征在于，所述的CNN缺陷检测模型包括一个主干特征提取网络VGG16模型和三个额外的用于预测结果的卷积分支组成；主干网络VGG16结果包含5个连续的由卷积层、池化层和激活函数组成的构建模块；这5个构建块共有13个层，用于从图像中提取特征；为了提高模型对小缺陷区域的检测精度，同时减少计算时间，额外的3个预测卷积分支直接从主干的VGG16网络中引出，在多尺度特征上检测缺陷。
  3. 根据权利要求2所述的高速高精度锂离子电池极片的毛刺检测方法 ，其特征在于，第一个预测卷积分支从主干特征提取网络VGG16模型的第5个构建模块之后引出，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果；其中，第5层输出的特征在用于预测结果之后还经过第一上采样层，扩大2倍后与来自第4个构建模块的特征进行融合，然后进入第二个预测卷积分支，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果；该融合后的特征再经过第二上采样层，扩大2倍后与来自第3个构建块的特征进行融合；然后进入第三个预测卷积分支，经过一个3×3卷积层提取特征，再由一个1×1卷积层预测结果。
  4. 根据权利要求3所述的高速高精度锂离子电池极片的毛刺检测方法，其特征在于，每个预测卷积分支在特征图的每个像素点位置预测出3个不同的疑似缺陷区域候选框。
  5. 一种高速高精度锂离子电池极片的毛刺检测系统，其特征在于：包括图像采集模块(1)和检测模块(2)，所述图像采集模块(1)包括光源(11)和线扫描相机(12)，所述光源(11)用于给极片预定部位进行照明，所述线扫描相机(12)对极片被照明后的部位进线扫描拍照，并将所拍的图像传输线所述检测模块(2)。
  6. 根据权利要求5所述的高速高精度锂离子电池极片的毛刺检测系统，其特征在于，所述检测模块(2)依据权利要求1-4中任一项所述高速高精度锂离子电池极片的毛刺检测方法对所拍的图像进行处理。
  7. 根据权利要求5或6所述的高速高精度锂离子电池极片的毛刺检测系统，其特征在于，所述光源(11)包括第一光源(111)和第二光源(112)，所述第一光源(111)设在第一安装件(113)上；所述第二光源(112)设在第二安装件(114)上，所述第一安装件(113)和第二安装件(114)的上端通过转轴(115)枢接，构成人字型光源，所述线扫描相机(12)设在人字型光源的中心线 上，且所述线扫描相机(12)的镜头对准所述光源(11)的照射区域。
  8. 根据权利要求5或6所述的高速高精度锂离子电池极片的毛刺检测系统，其特征在于，在远离所述光源(11)的一侧，极片的下方还设有平面背光源(13)，所述平面背光源(13)的发光方向朝向所述极片的下底面；用于区分毛刺缺陷与极片背景。
  9. 根据权利要求5或6所述的高速高精度锂离子电池极片的毛刺检测系统，其特征在于，所述图像采集模块(1)为两个，分别位于极片长度方向的两侧，用于采集极片两侧的毛刺。
  10. 根据权利要求5或6所述的高速高精度锂离子电池极片的毛刺检测系统，其特征在于，还包括喷气清洁装置(14)，所述喷气清洁装置(14)位于所述图像采集模块(1)的上游侧，用于对极片进行除尘。
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