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Title: Defect Detection Algorithm for Battery Cell Casings Based on Dual-Coordinate Attention and Small Object Loss Feedback
Authors: Li, T
Ren, J
Yang, Q
Chen, L
Sun, X
Keywords: low space ratio feature;small object feature;dual coordinate attention;small object loss feedback;defect detection of battery cell casings
Issue Date: 18-Mar-2024
Publisher: MDPI
Citation: Li, T. et al. (2024) 'Defect Detection Algorithm for Battery Cell Casings Based on Dual-Coordinate Attention and Small Object Loss Feedback', Processes, 12 (3), 601, pp. 1 - 16. doi: 10.3390/pr12030601.
Abstract: To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production.
Description: Data Availability Statement: Data are contained within the article.
Acknowledgements: The authors are grateful for the facilities and other support part by Shanghai Betterway for Automation Company.
Other Identifiers: ORCiD: Tianjian Li
ORCiD: Qingping Yang
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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