哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(4)：419-429,11.
RD-YOLO:改进YOLOv11的无人机图像小目标检测算法
RD-YOLO:improved drone image small target detection algorithm based on YOLOv11
摘要
Abstract
To address the challenges of detecting small objects against complex backgrounds and the low recognition accuracy in UAV images,this paper proposed an improved model named RD-YOLO,based on the YOLOv11 architecture,to enhance the detection performance for small targets in UAV imagery.The model incorporated receptive field attention convolution(RFAConv)into the backbone network during downsampling,combined with the C3k2 module to form a C3k2-RFA module,enhancing feature extraction while mitigating spatial information loss.Additionally,a dual-oriented feature pyramid network(DoFPN)was designed as a neck network to optimize multi-scale feature fusion by balancing spatial and semantic information.The C2PSA-LSKA module,integrating large separable kernel with attention,was introduced to effectively aggregate multi-scale contextual features.Experiments on the VisDrone2019 dataset demonstrated that RD-YOLO-s achieved 46.2%in mAP@50 and 27.8%in mAP@50∶95,surpassing YOLOv11s by 6.9%and 4.5%,respectively,validating its effectiveness and advancement in small object detection for UAV aerial imagery.关键词
无人机/小目标检测/感受野注意力卷积/多尺度特征融合/注意力机制Key words
UAV/small object detection/RFAConv/multi-scale feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
吴申奥,于瓅..RD-YOLO:改进YOLOv11的无人机图像小目标检测算法[J].哈尔滨商业大学学报（自然科学版）,2026,42(4):419-429,11.基金项目
安徽省重点研究与开发计划项目(202104d07020010). （202104d07020010）
