SF-YOLOv5: Improved YOLOv5 with swin transformer and fusion-concat method for multi-UAV detection

Abstract

When dealing with complex trajectories, and the interference by the unmanned aerial vehicle (UAV) itself or other flying objects, the traditional detecting methods based on YOLOv5 network mainly focus on one UAV and difficult to detect the multi-UAV effectively. In order to improve the detection method, a novel algorithm combined with swin transformer blocks and a fusion-concat method based on YOLOv5 network, so called SF-YOLOv5, is proposed. Furthermore, by using the distance intersection over union and non-maximum suppression (DIoU-NMS) as post-processing method, the proposed network can remove redundant detection boxes and improve the efficiency of the multi-UAV detection. Experimental results verify the feasibility and effectiveness of the proposed network, and show that the mAP trained on the two datasets used in experiments has been improved by 2.5 and 4.11% respectively. The proposed network can detect multi-UAV while ensuring accuracy and speed, and can be effectively used in the field of UAV monitoring or other types of multi-object detection applications.