Defect Detection of Intelligent Storage Items Based on YOLO Algorithm

In order to improve the management level of intelligent storage system, this paper proposes an article defect detection method based on improved YOLOv5 algorithm. Firstly, the CA attention mechanism is introduced into the YOLOv5 algorithm, which improves the detection accuracy. Then, the loss function CIoU is modified to WIoU v3 loss to improve the inference speed. The improved YOLOv5 algorithm was compared with the traditional R-CNN, YOLOv3, YOLOv4, YOLOv5 algorithms in the warehousing system, and the results showed that the proposed method had advantages in each index, which verified the feasibility of the scheme.