YOLO-DSC: a small target detection method for unmanned aerial vehicle images

Abstract Aiming at the phenomena of missing detection and false detection caused by multiple instances of small and medium-sized targets, occlusion between targets and rotation deviation of target positions, an improved YOLO11(You Only Look Once 11) based small target detection algorithm for UAV images is proposed. Firstly, the C3DC module is proposed by DynamicConv and C3k2 to enhance the expression ability of different features. Secondly, C2PDA is introduced into the neck by combining Dynamic Position Bias (DPB) module to reduce the sensitivity of the model to the target position deviation. Finally, SCO module is proposed to improve the feature pyramid structure to improve the detection ability of small targets. Through the experimental verification on the VisDrone2019 dataset, the experimental results show that compared with the original YOLO11 model, the mAP50 of the improved model is increased by 3.2% to 42.2%, and the MAP50:95 is increased by 2.2% to 25.6%.It achieves 33.7% mAP50 on the UAVDT dataset, which is also better than other SOTA models.Compared with the existing YOLO model and its improved version, the proposed model maintains high detection accuracy in complex scenarios. The research results show that the algorithm has significantly improved the detection ability of UAV images in complex scenes, and its comprehensive performance comprehensively exceeds that of YOLO11 model.