储能科学与技术2025，Vol.14Issue(1)：370-379,10.DOI:10.19799/j.cnki.2095-4239.2024.0591
BDD-DETR:高效感知小目标的锂电池表面缺陷检测
BDD-DETR:An efficient algorithm for detecting small surface defects on lithium batteries
摘要
Abstract
To address the challenges posed by the large scale and shape differences of defects on the end face of lithium battery casings,which complicate the detection of small target defects,we introduce a novel lithium battery surface defect detection algorithm based on battery defects detection-detection transformer(BDD-DETR).The BDD-DETR framework introduces a new feature perception and fusion network(FPFN)module between the general feature extraction and detection head modules.Through the adaptive feature perception module and the feature fusion path in FPFN,the deep and shallow features of this network from multiple directions are merged,the response of crucial feature information is enhanced,and redundant features are suppressed,which further improves the ability of the model to fuse multi-scale features and its capability to detect small objects.In addition,to minimize distance and shape deviations during defect bounding box regression,the shape intersection over union loss function is employed to train the network model.Experimental results indicate that on a constructed lithium battery end surface defect dataset,compared to the collaborative-detection transformer,BDD-DETR improves average precision by 3.7%,small-scale object detection precision by 8.9%,and average recall rate by 1.1%.Furthermore,BDD-DETR outperforms several advanced object detection approaches in detecting small defects in lithium batteries.关键词
锂离子电池/缺陷检测/Co-DETR/特征感知与融合网络/Shape IoU损失Key words
lithium-ion battery/defect detection/Co-DETR/feature perception and fusion network/Shape IoU loss分类
信息技术与安全科学引用本文复制引用
邢远秀,刘颛玮,邢玉峰,王文波..BDD-DETR:高效感知小目标的锂电池表面缺陷检测[J].储能科学与技术,2025,14(1):370-379,10.基金项目
企业委托科技项目(2023H20132). （2023H20132）
