URS-YOLOv5s: object detection algorithm for UAV remote sensing images

Abstract

Abstract The application of UAV (Unmanned Aerial Vehicle) remote sensing and aerial photography technology is more and more widely. Aiming at the problem of object detection in remote sensing or aerial images, the paper proposes an object detection algorithm for UAV remote sensing images based on YOLOv5s, called URS-YOLOv5s (UAV Remote Sensing - YOLOv5s). Firstly, the paper designs Cross-Connected Dense Network (CCDNet), where each convolution layer is concatenated to the second convolution layer. Secondly, the paper designs Across-Path Fusion Network (APFNet), which the last feature fusion path is directly fused with the backbone network. It increases the location information and semantic information of deep features. Finally, the loss function of the original algorithm is replaced by EIoU (Focal and Efficient IoU), which makes the calculation method of the overall loss more appropriate. On AI-TOD and Visdrone2019 datasets, experiments show that the accuracy of URS-YOLOv5s on mAP@0.5 is 6.3% and 9% higher than YOLOv5s. In addition, compared with YOLOv3 and YOLOv5l which have good detection effect, URS-YOLOv5s has the characteristics of faster detection speed and lower computational cost. Meanwhile, URS-YOLOv5s is more suitable for deployment to mobile devices, such as drones with limited performance.