Abstract: Aiming at the problems of low detection accuracy caused by small object sizes,insufficient feature information,dense distribution,and occlusion in UAV aerial photography,this paper proposes a UAV small object detection algorithm based on feature enhancement and context fusion.Firstly,a lightweight backbone network for enhanced feature extraction is constructed,utilizing lightweight feature extraction blocks to efficiently extract feature information,and a fine-grained channel fusion block is designed to effectively prevent the loss of fine-grained features.The backbone network improves the feature extraction capability and inference speed of the model.Secondly,a small object detection head is constructed to fully extract the position information and detailed features of small objects.Then,the adaptive spatial attention module is used to adaptively adjust the receptive fields required for different objects,making full use of the rich context information around the aerial small objects.Finally,a minimum point distance-based bounding box regression loss function(MPDIoU) is introduced to further improve the precision of dense small object detection.The proposed algorithm achieves mAP_0.5 and mAP_0.5:0.95 of 46.7% and 28.6% on the VisDrone2019 dataset,respectively,representing an improvement of 8.5% and 5.9% over the baseline network YOLOv8s.Moreover,the algorithm reduces parameters by 23.4% compared to YOLOv8s,making it efficient for dense small object detection in UAV aerial photography scenarios.

Key words: Unmanned Aerial Vehicle, Small object detection, Lightweight networks, Context information, Attention mechanism