A New Algorithm for Small Target Detection From the Perspective of Unmanned Aerial Vehicles

Abstract

Since the UAV is far away from the detected target when performing target detection at high altitudes, there is a significant difference in the size of the detected object, and there are problems such as the detected target being blocked by the object. This paper proposed an improved algorithm BDH-YOLO for UAV high-altitude small target detection based on YOLOv8s. The backbone network of YOLOv8s is used, and the weighted bidirectional feature pyramid network (BiFPN) is combined with the backbone network to enhance the integration of multi-level features and obtain more multi-scale semantic information. Deal with multiple downsampling of the feature map will lead to the reduction of the resolution of the feature map, which will make the pixels on the feature map overlap, thus losing a large amount of spatial information, a dynamic detection head DyHead (Dynamic Head) combined with self-attention is introduced, which significantly improves the performance of the object detector by combining the perception capabilities of the three dimensions of scale perception, spatial perception, and task perception. Experimental results show that on the VisDrone2019-DET dataset, Compared with the YOLOv8s model, the BDH-YOLO model has an average accuracy (mAP@0.5) improvement of 3 targets than other mainstream models.