Minimal Defect Detection on End Face of Lithium Battery Shells

Yufeng Xing, Qi Liu, Yuanxiu Xing (), Zhuanwei Liu and Wenbo Wang
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Yufeng Xing: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Qi Liu: Computer Science of Technology, Anhui University, Hefei 230601, China
Yuanxiu Xing: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Zhuanwei Liu: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Wenbo Wang: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China

Sustainability, 2024, vol. 16, issue 23, 1-16

Abstract: Lithium batteries represent a pivotal technology in the advancement of renewable energy, and their enhanced performance and safety are vital to the attainment of sustainable development goals. To solve the issue of the high missed detection rate of minimal defects on end face of lithium battery shells, a novel YOLO-based Minimal Defect Detection algorithm, named YOLO-MDD, is proposed. Firstly, aimed at the problem of insufficient data, a dataset of defects on the end face of lithium battery shells is constructed and annotated. Secondly, a YOLO-MDD network which includes a feature extraction module and a four-scale detection head for detecting defects of various scales is improved. Here, deformable convolution and an attention module are ingeniously embedded into the backbone of YOLO to capture more detailed and accurate information on object defects, and the four-scale head is used to handle the significant differences in the size and shape of defects on lithium battery shells. Finally, a hybrid loss including localization loss with normalized Wasserstein distance (NWD), classification loss, and confidence loss is designed to optimize our model to further enhance its sensitivity to minimal defects. The experimental results show that the proposed YOLO-MDD has a mean average precision of 80% for the defect detection of the lithium battery shells, especially with a minimal defect rust spots mean average precision of 74.1% and a recall rate of 71.5%, which is superior compared with other mainstream detection algorithms and provides the technical support necessary to achieve the goals of energy and environmental sustainability.

Keywords: lithium batteries; battery surface defect detection; sustainability; energy storage; hybrid loss; small object detection network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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