To address the challenges of small object detection in UAV aerial imagery—such as dense distribution, scale variation, occlusion, and complex backgrounds—this paper proposes RAD-YOLO, an improved lightweight detection framework based on YOLOv11n. The model incorporates a RFM-FPN with RAU and SBA to enhance multi-scale feature integration. It also employs RFAConv in the backbone for adaptive spatial modeling, and introduces DDS-Soft-NMS strategy to reduce false suppression based on object scale. Experimental results show that RAD-YOLO improves mAP@0.5 and mAP@0.5:0.95 by 13.1% and 11.4% respectively on the VisDrone2019 dataset, achieving 0.561 precision and 0.411 recall. On AI-TOD and SODA-A datasets, mAP@0.5 improvements of 9.9% and 7.7% further demonstrate its robustness and strong generalization in complex aerial scenarios.