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.