一种改进YOLOv8s的无人机航拍目标检测方法OA

Object detection in unmanned aerial vehicle(UAV)aerial scenarios is challenging due to the wide variation in object scales,dense distributions,and some object features are not obvious.General object detection algorithms struggle to accurately capture the object regions in such scenes,leading to low detection accuracy.To address this problem,an improved object detection method based on YOLOv8s is proposed for UAV aerial scenarios.By introducing a deformable attention mechanism,the receptive field is adaptively adjusted to improve the model's perception of object geometry.Feature enhancement is achieved through the attention-based intra-scale feature interaction module,which dynamically integrates local and global feature information,strengthening the model's feature representation.Additionally,the EIoU loss is adopted to jointly optimize object position and shape,reducing false positives and missed detections.Experimental results on the VisDrone2019 dataset demonstrate significant improvements,with precision increased by 2.5%,recall improved by 1.7%,and mean average precision(mAP)enhanced by 2.3%compared with YOLOv8s.Additionally,the proposed method outperforms other state-of-the-art models.Through the visualization effect verification,the proposed method shows superior performance in dense scenes,multi-scale objects and night scenes.