南京邮电大学学报（自然科学版）2026，Vol.46Issue(3)：62-69,8.DOI:10.14132/j.cnki.1673-5439.2026.03.007
基于改进YOLOv11的无人机雾天车辆目标检测算法研究
UAV vehicle target detection in foggy weather based on improved YOLOv11
摘要
Abstract
Since unmanned aerial vehicles(UAVs)often suffer low multi-scale target detection accuracy,insufficient feature contrast and limited computing resources caused by image degradation in foggy scenes,this paper proposes a lightweight foggy vehicle detection algorithm YOLO-RDPF based on im-proved YOLOv11.First,the C3K2_RCB module and the DWConv module are introduced into the back-bone network to enhance its feature extraction ability.Second,the SimAM module is incorporated to high-light key features and suppress redundant information.Finally,a feature focusing dispersion feature pyra-mid network(FDFPN)is constructed to further improve the multi-scale feature fusion and representation.The experimental results on the HazyDet dataset show that the YOLO-RDPF algorithm achieves the mAP50 and mAP50-95 of 3.1%and 2.5%,respectively,which are both higher than those of the YOLOv11n algorithm.The mAP50 reaches 68.3%,while the number of model parameters is only 1.8×106.These re-sults demonstrate that YOLO-RDPF can significantly improve the vehicle detection performance in foggy scenes while maintaining a high degree of lightweight.关键词
目标检测/轻量化网络/雾天/聚焦机制/YOLOv11Key words
target detection/lightweight network/foggy day/focusing mechanism/YOLOv11分类
信息技术与安全科学引用本文复制引用
王诚,谢立云,李坤..基于改进YOLOv11的无人机雾天车辆目标检测算法研究[J].南京邮电大学学报（自然科学版）,2026,46(3):62-69,8.基金项目
国家自然科学基金(62371245)资助项目 （62371245）
