基于LO-RANSAC的锂电池极片表面痕类缺陷检测

doi:10.19799/j.cnki.2095-4239.2022.0678

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (2): 593-601.doi: 10.19799/j.cnki.2095-4239.2022.0678

基于LO-RANSAC的锂电池极片表面痕类缺陷检测

姜宝超¹^,²(), 曾勇¹^,²(), 韩有军¹^,², 胡跃明¹^,²

^1.华南理工大学自动化科学与工程学院
^2.精密电子制造装备教育部工程研究中心/广东省高端芯片智能封测装备工程实验室，广东广州 510641

收稿日期:2022-11-16 修回日期:2022-11-21 出版日期:2023-02-05 发布日期:2023-02-24
通讯作者: 曾勇 E-mail:jiangbcy@163.com;auyzeng@scut.edu.cn
作者简介:姜宝超（1998—），男，硕士研究生，研究方向为图像处理和深度学习，E-mail：jiangbcy@163.com；
基金资助:
国家重大科技专项02专项(2014ZX02503)

Scratch defect detection of lithium battery electrode based on LO-RANSAC algorithm

Baochao JIANG¹^,²(), Yong ZENG¹^,²(), Youjun HAN¹^,², Yueming HU¹^,²

^1.College of Automation Science and Engineering, South China University of Technology
^2.Enginering Research Center for Precision Electronic Manufacturing Equipment, Ministry of Education & Guangdong Provincial Engineering Laboratory for Advanced Chip Intelligent Packaging Equipment, South China University of Technology, Guangzhou 510641, Guangdong, China

Received:2022-11-16 Revised:2022-11-21 Online:2023-02-05 Published:2023-02-24
Contact: Yong ZENG E-mail:jiangbcy@163.com;auyzeng@scut.edu.cn

摘要/Abstract

摘要：

针对锂电池极片表面的痕类缺陷检测准确率低、误检率和漏检率高的问题，提出了一种基于局部最优化的随机抽样一致性(locally optimized random sample consensus， LO-RANSAC)的痕类缺陷检测算法。首先，针对锂电池极片表面存在的椒盐噪声、大噪点多的问题，提出了一种改进的自适应中值滤波和基于连通域的滤波算法。其次，针对检测痕类缺陷准确率达不到预期以及误检率漏检率较高的问题，引入一种局部最优化的RANSAC算法。最后，给出了一种基于LO-RANSAC的痕类缺陷分类方法。实验结果表明：本文所提算法相较于标准RANSAC检测准确率提高了5.9%，相较于基于卷积神经网络算法准确率提高了15%，达到了98.2%；多种算法中本工作算法对于痕类缺陷的检测误检率和漏检率最低；平均检测速度较标准RANSAC算法提高了1.7倍，每秒钟检测的图片数量FPS(frame per second)达到12.49。本工作算法具有较高的检测准确率、较低的误检率及漏检率，检测速度达到实时检测要求，因此可满足锂电池极片表面的痕类缺陷检测需求，解决了锂电池极片表面痕类缺陷自动检测难题。

关键词: 痕类缺陷, 自适应中值滤波, RANSAC, 缺陷检测

Abstract:

An algorithm based on locally optimized RANSAC (locally optimized random sample consensus, LO-RANSAC) is recommended for the problems of low trace defect detection accuracy, high false detection rate, and high missed detection rate of scratch defect detection on the surface of a lithium battery. First, in response to the problem of pepper noise, large noises that exist on the scratch defects of lithium batteries, a filtering algorithm based on improved adaptive median filtering and connected domains filtering is proposed. Secondly, to address the problems that the detection accuracy of detecting trace defects does not meet expectations and the false detection and missed detection rates are high, a locally optimized RANSAC algorithm is introduced. Finally, a scratch defect classification based on the LO-RANSAC algorithm is proposed. The experimental results demonstrate that when compared to the standard RANSAC algorithm, the proposed algorithm's average detection accuracy is increased by 5.9%, when compared to the convolution-based neural network algorithm is increased by more than 15%, reaching 98.2%. Among several algorithms, the algorithm achieves the lowest false positive and false negative rate for trace defects. The average detection speed is 1.7 times faster than the standard RANSAC algorithm, with an FPS (frame per second) of 12.49 images detected per second. The proposed algorithm has a high detection accuracy, a low false detection rate, and missed detection rate, and a detection speed that meets real-time detection requirements, allowing it to meet the detection needs of trace defect on the surface of lithium battery pole pieces and solve the problem of automatic detection of trace defects on the surface of lithium battery pole pieces.

Key words: scratch defect, adaptive median filtering, RANSAC, defect detection

中图分类号:

TP 391.41

姜宝超, 曾勇, 韩有军, 胡跃明. 基于LO-RANSAC的锂电池极片表面痕类缺陷检测[J]. 储能科学与技术, 2023, 12(2): 593-601.

Baochao JIANG, Yong ZENG, Youjun HAN, Yueming HU. Scratch defect detection of lithium battery electrode based on LO-RANSAC algorithm[J]. Energy Storage Science and Technology, 2023, 12(2): 593-601.

图/表 8

图1

图2

图3

图4

图5

图6

图7

表1

参考文献 20

1	汤匀, 岳芳, 郭楷模, 等. 全固态锂电池技术发展趋势与创新能力分析[J]. 储能科学与技术, 2022, 11(1): 359-369.
	TANG Y, YUE F, GUO K M, et al. Analysis of the development trend and the innovation ability of an all-solid-state lithium battery technology[J]. Energy Storage Science and Technology, 2022, 11(1): 359-369.
2	胡振恺, 雷博, 李勇琦, 等. 储能用锂离子电池安全性测试与评估方法比较[J]. 储能科学与技术, 2022, 11(5): 1650-1656.
	HU Z K, LEI B, LI Y Q, et al. Comparative study on safety test and evaluation methods of lithium-ion batteries for energy storage[J]. Energy Storage Science and Technology, 2022, 11(5): 1650-1656.
3	马彪, 林春景, 刘磊, 等. 锂离子电池热失控产气特性及其可燃极限[J]. 储能科学与技术, 2022, 11(5): 1592-1600.
	MA B, LIN C J, LIU L, et al. Venting characteristics and flammability limit of thermal runaway gas of lithium ion battery[J]. Energy Storage Science and Technology, 2022, 11(5): 1592-1600.
4	黄梦涛, 连一鑫. 基于改进Canny算子的锂电池极片表面缺陷检测[J]. 仪器仪表学报, 2021, 42(10): 199-209.
	HUANG M T, LIAN Y X. Lithium battery electrode plate surface defect detection based on improved Canny operator[J]. Chinese Journal of Scientific Instrument, 2021, 42(10): 199-209.
5	孙浩然, 李雅雯, 韩有军, 等. 基于拓扑滤波与改进Canny算子的锂离子电池电极缺陷检测[J]. 储能科学与技术, 2022, 11(10): 3297-3305.
	SUN H R, LI Y W, HAN Y J, et al. Lithium-Ion battery electrode defect detection based on topological filtering and improved canny operator[J]. Energy Storage Science and Technology, 2022, 11(10): 3297-3305.
6	XU J M, LIU Y, XIE H W, et al. Surface quality assurance method for lithium-ion battery electrode using concentration compensation and partiality decision rules[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 3157-3169.
7	LIU Y, CHEN Y, XU J M. An automatic defects detection scheme for lithium-ion battery electrode surface[C]//2020 International Symposium on Autonomous Systems (ISAS). December 6-8, 2020, Guangzhou, China. IEEE, 2021: 94-99.
8	SONG L M, LIN W W, YANG Y G, et al. Weak micro-scratch detection based on deep convolutional neural network[J]. IEEE Access, 2019, 7: 27547-27554.
9	张建国, 李颖, 齐家坤, 等. 基于机器视觉的手机屏幕表面划痕检测研究[J]. 应用光学, 2020, 41(5): 984-989.
	ZHANG J G, LI Y, QI J K, et al. Surface scratch detection of mobile phone screen based on machine vision[J]. Journal of Applied Optics, 2020, 41(5): 984-989.
10	TAO X, ZHANG D P, HOU W, et al. Industrial weak scratches inspection based on multifeature fusion network[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-14.
11	SAGAR P, UPADHYAYA A, MISHRA S K, et al. A circular adaptive Median filter for salt and pepper noise suppression from MRI images[J]. Journal of Scientific & Industrial Research, 2020, 79(10): 941-944.
12	VESTAL B E, CARLSON N E, GHOSH D. Filtering spatial point patterns using kernel densities[J]. Spatial Statistics, 2021, 41, doi: 10.1016/j.spasta.2020.100487.
13	LIU J. Feature recognition method for similar key points of human face based on adaptive Median filter[J]. International Journal of Biometrics, 2021, 13(1): 1-16.
14	梁宇峰. 太阳暗条手征性自动识别的方法研究[D]. 昆明: 昆明理工大学, 2019.LIANG Y F. Study on the method of chiral automatic recognition of dark bar in the sun[D]. Kunming: Kunming University of Science and Technology, 2019.
15	HOSSEIN-NEJAD Z, NASRI M. A-RANSAC: Adaptive random sample consensus method in multimodal retinal image registration[J]. Biomedical Signal Processing and Control, 2018, 45: 325-338.
16	TORR P H S, ZISSERMAN A. MLESAC: A new robust estimator with application to estimating image geometry[J]. Computer Vision and Image Understanding, 2000, 78(1): 138-156.
17	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence. June 6, 2016, IEEE, 2016: 1137-1149.
18	REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. 2018[2022-10-01]. https://arxiv.org/abs/1804.02767
19	刘思军. 运动姿态视频测量分析仪高速JPEG译码器设计[D]. 绵阳: 西南科技大学, 2021.LIU S J. Design of high speed JPEG decoder for motion posture video measurement analyzer[D]. Mianyang: Southwest University of Science and Technology, 2021.
20	郭怀勇. 柔性IC封装基板外观缺陷检测问题研究[D]. 广州: 华南理工大学, 2020.GUO H Y. Research on the surface defects detection of flexible integrated circuit substrate[D]. Guangzhou: South China University of Technology, 2020.

算法	ACC	FPS	FPR	FNR
标准RANSAC	92.3%	4.69	22.28%	1.09%
Faster-RCNN	81.4%	14.93	37.93%	5.47%
YOLOv3	83.2%	45.30	33.33%	3.54%
改进的RANSAC	98.2%	12.49	5.38%	0.54%

基于LO-RANSAC的锂电池极片表面痕类缺陷检测

Scratch defect detection of lithium battery electrode based on LO-RANSAC algorithm

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 20

相关文章 1

编辑推荐

Metrics

本文评价