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

doi:10.19799/j.cnki.2095-4239.2022.0678

Abstract

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

CLC Number:

TP 391.41

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.

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 1

References 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%