关键词: 深度学习, 目标检测, YOLO, Shuffle, SA, 注意力机制, K-means

Abstract: In order to improve the efficiency of detecting surface defects on industrial parts, a target detection method based on improved YOLOv3 is proposed. The latest attention mechanism SA (Shuffle Attention) with channel shuffling is introduced and combined with the residual unit of the Darknet-53 backbone structure of the YOLOv3 model to form the SA residual block structure, which fully utilizes the feature channel information to obtain the YOLOv3-SA model. For different datasets, the input images are scaled at different scales, and the K-means method is used to cluster the real bounding boxes to improve detection efficiency. The experimental results show that the recall rate of the YOLOv3-SA model reaches 95.4%, and the mAP can be increased by up to 7% compared to YOLOv3.

Key words: deep learning, target detection, YOLO, Shuffle；shuffle attention, attention mechanism, K-means