电池2025，Vol.55Issue(6)：1248-1256,9.DOI:10.19535/j.1001-1579.2025.06.005
BCDD-YOLO算法检测锂离子电池顶盖缺陷
Li-ion battery top cover defect detection using BCDD-YOLO algorithm
摘要
Abstract
When detecting the surface defects of Li-ion battery top cover,the detection accuracy for irregular small targets is low under complex lighting conditions,and there are challenges in terms of lightweighting and real-time performance.A battery cover defect detection(BCDD)-YOLO algorithm is proposed.Firstly,partial convolution is introduced instead of the conventional convolution to construct a lightweight point-wise convolution and partial convolution cooperative shuffling mixed convolution module PPW-Conv,which reduces computational redundancy and memory access;Secondly,in the feature fusion part,a linearly deformable large kernel attention module LD-LKA is proposed,combining the attention mechanism of large convolution kernels and linear deformable convolutions,to widely and flexibly capture the context information of the image;Finally,a more effective loss function InnerFocaler-CIoU that focuses more on the samples is used instead of the complete intersection-over-union CIoU loss function,an auxiliary bounding box is used to accelerate the regression of bounding boxes,improving the detection accuracy and robustness of small targets.Experiments show that this method significantly improves the detection accuracy compared to mainstream algorithms,the average precision mean mAP on the Li-ion battery top cover defect dataset reaches 75.5％,fully verifying the effectiveness of the improved algorithm.关键词
锂离子电池/顶盖/缺陷检测/大卷积核/YOLOv9/BCDD-YOLO/LD-LKA/InnerFocaler-CIoUKey words
Li-ion battery/top cover/defect detection/large convolution kernel/YOLOv9/BCDD-YOLO/LD-LKA/InnerFocaler-CIoU分类
信息技术与安全科学引用本文复制引用
刘志辉,曹丽丽,朱勇建..BCDD-YOLO算法检测锂离子电池顶盖缺陷[J].电池,2025,55(6):1248-1256,9.基金项目
教育部中国高校产学研创新基金(2024HY010),浙江省"十四五"教学改革项目(jg20220405) （2024HY010）
