An Improved YOLOv5 Model for Detecting Laser Welding Defects of Lithium Battery Pole

Abstract

Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved YOLOv5 algorithm was proposed in this paper. First, all the 3 × 3 convolutional kernels in the backbone network were replaced by 6 × 6 convolutional kernels to improve the model’s detection capability of a small defect; second, the last layer of the backbone network was replaced by our designed SPPSE module to enhance the detection accuracy of the model; then the improved RepVGG module was introduced in the head network, which can help to improve the inference speed of the model and enhance the feature extraction capability of the network; finally, SIOU was used as the bounding box regression loss function to improve the accuracy and training speed of the model. The experimental results show that our improved YOLOv5 model achieved 97% mAP and 270 fps on our dataset. Compared with conventional methods, ours had the best results. The ablation experiments were conducted on the publicly available datasets PASCAL VOC and MS COCO, and their mAP@0.5 was improved by 2.4% and 3%, respectively. Additionally, our model improved the average detection rate for small targets on the MS COCO dataset by 2.4%, showing that it can effectively detect small target defects.