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	Few-shot learning approach for 3D defect detection in lithium battery
	Wu,Ke1,2; Tan,Jie1; Li,Jingwei1,2; Liu,Chengbao1
刊名	Journal of Physics: Conference Series
	2021-04-01
卷号	1884期号:1
ISSN号	1742-6588
DOI	10.1088/1742-6596/1884/1/012024
英文摘要	Abstract Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, the limitation of acquiring the 3D information, and the shortage of massive amounts of labelled training data make the 2D detection method hard to classify surface defects. In this work, a few-shot learning approach for 3D defect detection in lithium batteries is proposed. The multi-exposure-based structured light method is introduced to reconstruct the 3D shape of the lithium battery. Then, the anomaly part of the 3D point cloud is transferred into 2D images by the height-gray transformation. The MiniImageNet datasets are used as the source domain to pretrain the Cross-Domain Few-Shot Learning (CD-FSL) model. The accuracy in our experiment result is 97.17%, which means that our method can be used to classify the surface defects of the lithium battery.
语种	英语
出版者	IOP Publishing
WOS记录号	IOP:1742-6588-1884-1-012024
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44284]
专题	综合信息系统研究中心_工业智能技术与系统
作者单位	1.Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of artificial intelligence, University of Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Wu,Ke,Tan,Jie,Li,Jingwei,et al. Few-shot learning approach for 3D defect detection in lithium battery[J]. Journal of Physics: Conference Series,2021,1884(1).
APA	Wu,Ke,Tan,Jie,Li,Jingwei,&Liu,Chengbao.(2021).Few-shot learning approach for 3D defect detection in lithium battery.Journal of Physics: Conference Series,1884(1).
MLA	Wu,Ke,et al."Few-shot learning approach for 3D defect detection in lithium battery".Journal of Physics: Conference Series 1884.1(2021).

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