Research progress on defect detection of lithium battery based on machine vision

被引：0
作者：
Yu, Hanwen [1 ]
Wu, Yiquan [1 ]
机构：
[1] College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2024年 / 45卷 / 09期
关键词：
deep learning; defect detection; image process; lithium battery; machine vision; performance evaluation index;
D O I：
10.19650/j.cnki.cjsi.J2412901
中图分类号：
学科分类号：
摘要：
Lithium battery is one of the core components of new energy vehicles. But, the complex manufacturing process of lithium battery inevitably introduces various defects, which seriously affects the quality of products. Therefore, defect detection has become an important part of lithium battery manufacturing process. The machine vision method takes into account the advantages of accuracy and speed, which has been paid much attention to. In this article, the research progress of defect detection methods for lithium battery based on machine vision in recent 15 years is reviewed. Firstly, the common surface defect types of lithium battery are introduced, and the main flow of visual defect detection is clarified. Next, the defect detection method of lithium battery based on traditional image processing is emphasized. The four steps, including image preprocessing, image segmentation, feature extraction and classification recognition, are explained in detail. The advantages and disadvantages of each step are compared. Then, the defect detection methods based on deep learning are summarized according to the classification network, detection network and segmentation network. Afterwards, 10 self-built datasets of lithium battery and performance evaluation index of defect detection are sorted out. Finally, it is pointed out that the defect detection of lithium battery is faced with many technical challenges, and the future work is prospected. © 2024 Science Press. All rights reserved.
引用
收藏
页码：1 / 23
页数：22
相关论文
共 104 条
[61]  
MA L Y, XIE W, ZHANG Y., Blister defect detection based on convolutional neural network for polymer lithium-ion battery, Applied Sciences, 9, 6, (2019)
[62]  
JIANG SH B, WANG T T, ZHANG SH L, Et al., Battery panel defect detection method based on deep convolutional neural network [ C ], 2019 IEEE 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1953-1958, (2019)
[63]  
PENG J W, XUE M M, LOU Y J., Automatic internal wrinkles detection of lithium-ion batteries using convolutional neural network [ C ], 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp. 1422-1427, (2021)
[64]  
DIN N U, ZHANG L, ZHOU Y, Et al., Laser welding defects detection in lithium-ion battery poles [ J ], Engineering Science and Technology, An International Journal, 46, (2023)
[65]  
GIRSHICK R, DONAHUE J, DARRELL T, Et al., Rich feature hierarchies for accurate object detection and semantic segmentation [ C ], 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580-587, (2014)
[66]  
GIRSHICK R., Fast R-CNN [ C ], 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, (2015)
[67]  
REN SH Q, HE K M, GIRSHICK R, Et al., Faster RCNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 6, pp. 1137-1149, (2016)
[68]  
LI R K., Surface defect detection for lithium battery based on multi-scale features, (2019)
[69]  
FENG X Y., Recognition and classification of surface defects of cylindrical lithium battery steel shell based on deep learning, (2020)
[70]  
CAI ZH W, VASCONCELOS N., Cascade R-CNN: Delving into high quality object detection, 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6154-6162, (2018)
234567891011