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Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach | |
Wu,Ke1; Tan,Jie2 | |
发表期刊 | Journal of Physics: Conference Series |
ISSN | 1742-6588 |
2021-11-01 | |
卷号 | 2078期号:1页码:012037 |
摘要 | Abstract In this paper, we propose a triplet siamese model for lithium-ion battery defects classification. It is a difficult task to detect the surface defects of lithium-ion batteries with stainless steel surface. The lack of three-dimensional information and the lack of marker datasets due to reflections prevent two-dimensional computer vision detection methods from meeting classification needs. In this work, the multiple exposure structured light method is utilized to obtain the three-dimensional shape of a lithium-ion battery with a stainless steel surface. The defect point cloud with three-dimensional information is obtained by this method, and then the 3D information of the defect point cloud is converted into grayscale information, and the grayscale image is used as the target domain data of the triplet siamese network. The public dataset MiniImageNet is utilized as the training data of the triplet siamese network model. The accuracies of the experimental results are 88.9%, 95.6%, and 97.8% for 1-shot, 5-shot, and 10-shot respectively. This result proves that our method can be used for lithium-ion battery defect detection. |
DOI | 10.1088/1742-6596/2078/1/012037 |
收录类别 | EI |
语种 | 英语 |
资助项目 | National Nature Science Foundation of China[U1701262] ; National Natural Science Foundation of China[U1801263] ; National Natural Science Foundation of China[U1801263] ; National Nature Science Foundation of China[U1701262] |
WOS记录号 | IOP:JPCS_2078_1_012037 |
出版者 | IOP Publishing |
七大方向——子方向分类 | 数据挖掘 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46374 |
专题 | 中科院工业视觉智能装备工程实验室_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 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu,Ke,Tan,Jie. Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach[J]. Journal of Physics: Conference Series,2021,2078(1):012037. |
APA | Wu,Ke,&Tan,Jie.(2021).Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach.Journal of Physics: Conference Series,2078(1),012037. |
MLA | Wu,Ke,et al."Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach".Journal of Physics: Conference Series 2078.1(2021):012037. |
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