中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Cross-domain few-shot learning approach for lithium-ion battery surface defects classification using an improved siamese network

文献类型:期刊论文

作者Wu, Ke1,2; Tan, Jie1; Liu, Cheng Bao1
刊名IEEE SENSORS JOURNAL
出版日期2022
页码1-1
关键词Few-shot Learning 3D measurement defect detection image classification
ISSN号1530-437X
DOI10.1109/JSEN.2022.3161331
英文摘要

It is difficult to detect the surface defects of a lithium battery with an aluminum/steel shell. The reflectivity, lack of 3D information on the battery surface, and the shortage of many datasets make the 2D detection method hard to apply in this field. In this paper, a cross-domain few-shot learning (FSL) approach for lithium-ion battery defect classification using an improved siamese network (BSR-SNet) is proposed. To obtain the critical 3D surface of the lithium-ion battery, a multiexposure-based structured light method is utilized. Then, the heights of the 3D cloud points are transferred to grayscale information and are saved as 8-bit 2D images. For the FSL task, the DAGM 2007 datasets are used as the source domain to pre-train the improved siamese model. To avoid negative mitigation in the target domain, batch spectral regularization (BSR) is added as a penalizer in the loss function. The accuracies of the experimental results are 93.3 % for 10-shot batteries and 91.0 % for 5-shot batteries, which means that our method can be used to classify the surface defects of lithium batteries well.

语种英语
源URL[http://ir.ia.ac.cn/handle/173211/48552]  
专题综合信息系统研究中心_工业智能技术与系统
通讯作者Tan, Jie; Liu, Cheng Bao
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of ChineseAcademy of Sciences
推荐引用方式
GB/T 7714
Wu, Ke,Tan, Jie,Liu, Cheng Bao. Cross-domain few-shot learning approach for lithium-ion battery surface defects classification using an improved siamese network[J]. IEEE SENSORS JOURNAL,2022:1-1.
APA Wu, Ke,Tan, Jie,&Liu, Cheng Bao.(2022).Cross-domain few-shot learning approach for lithium-ion battery surface defects classification using an improved siamese network.IEEE SENSORS JOURNAL,1-1.
MLA Wu, Ke,et al."Cross-domain few-shot learning approach for lithium-ion battery surface defects classification using an improved siamese network".IEEE SENSORS JOURNAL (2022):1-1.

入库方式: OAI收割

来源:自动化研究所

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