关键词: 目标检测, YOLOv3算法, Attention-YOLO算法, 通道注意力机制, 空间注意力机制

Abstract: YOLOv3 is a real-time object detection algorithm, its speed and accuracy reach good trade-off, but the disadvantages are that the boundary box positioning is inaccurate and it is difficult to distinguish overlapping objects. For the above problems, this paper proposes the Attention-YOLO algorithm based on the item-wise attention mechanism which embeds channel and spatial attention mechanism in the feature extraction network, uses the filtered weighted feature vector to replace the original residual fusion, and adds a second-order item to reduce the information loss in the process of fusion and accelerate the convergence of the model. Based on the experiments on COCO and PASCAL VOC datasets, the results show that the Attention-YOLO algorithm effectively reduces the boundary box positioning loss and improves the detection accuracy. Compared with YOLOv3, the Attention-YOLO improves at most 2.5 mAP@IoU[0.5∶0.95] on COCO dataset, and reaches 81.9 mAP on PASCAL VOC 2007 test.

Key words: object detection, YOLOv3 algorithm, Attention-YOLO algorithm, channel attention, spatial attention