Improved YOLOv5s Small Object Detection Algorithm in UAv view

Aiming at the problems such as the long distance between UAV and object in flight, the obvious difference in the size of the photographed object and the existence of object occlusion, an improved algorithm BD-YOLO based on YOLOv5s for small object detection under UAV perspective is proposed. In the feature fusion network, bi-level routing attention (BRA) is used to filter the least relevant features in the feature map in a dynamic sparse way, and retain some important regional features, so as to improve the feature extraction ability of the model. Since the feature map will lose a lot of location and feature information after multiple subsampled, a dynamic object detection head (DyHead) combining attention mechanism is adopted. The DyHead integrates scale perception, space perception and task perception to achieve stronger feature representation capability. Focal-EIoU Loss function is used to solve the problem of inaccurate regression results of CIoU Loss calculation in YOLOv5s, so as to improve the detection accuracy of the model for small object. The experimental results show that on the VisDrone2019-DET dataset, the BD-YOLO model has increased the mean average precision (mAP) index by 0.062 compared with the YOLOv5s model, and has better results for small object detection than other mainstream models. © 2024 Computer Engineering and Applications.