改进 YOLOv5 的新能源电池集流盘缺陷检测方法

针对新能源汽车电池集流盘中因目标缺陷分布杂乱、尺寸跨度大和特征模糊而易出现误检、漏检的问题,提出一种基于 多尺度可变形卷积的 YOLOv5 方法(YOLOv5s-4Scale-DCN),以用于汽车电池集流盘缺陷检测。 首先,针对不同尺度的缺陷目 标,在 YOLOv5 模型的基础上新增检测层,通过捕获不同尺度缺陷的特征以及融合不同深度的语义特征,提高对不同尺度缺陷 目标的检测率;其次,引入可变形卷积,扩大特征图的感受野,使提取的特征辨析力更强,有效地提高了模型的缺陷识别能力。 实验结果表明,所提的 YOLOv5s-4Scale-DCN 算法可以有效检测新能源汽车电池集流盘缺陷,mAP 达到了 91%,相较原算法提 高了 2. 5%,FPS 达到了 113. 6,重度不良和无盖缺陷这两种类别的缺陷,检测召回率达到了 100%,满足新能源汽车电池集流盘 缺陷实时检测要求。

Defect detection method for new energy battery collector disc based on improved YOLOv5 network

In order to solve the problem of false detection and missing detection in new-energy vehicle battery collector disk due to disarranged target defect distribution, large size span and fuzzy features, a YOLOv5 method based on multi-scale deformations convolution (YOLOv5s-4Scale-DCN) was proposed for defect detection of vehicle battery collector disk. Firstly, for defect targets of different scales, a new detection layer is added based on the YOLOv5 model. By capturing defect features of different scales and integrating semantic features of different depths, the detection rate of defect targets of different scales is improved. Secondly, deformable convolution is introduced to enlarge the receptive field of the feature map, which makes the extracted feature discrimination stronger and effectively improves the defect recognition ability of the model. Experimental results show that the proposed YOLOv5s-4Scale-DCN algorithm can effectively detect the defects of new-energy vehicle battery collection panel, with mAP up to 91%, 2. 5% higher than that of the original algorithm, and the FPS reaches 113. 6. There are two types of defects, severe defects and uncovered defects. The detection and recall rate reached 100%, meeting the requirements of real-time detection of the defects of the battery collecting disk of new energy vehicles.