A YOLO-Based Multi-Scale and Small Object Detection Framework for Low-Altitude UAVs in Cluttered Scenes

Low-altitude UAVs pose increasing security concerns due to their small sizes, high mobility, and low observability. Detecting such targets in cluttered environments remains challenging due to strong background interference and significant scale variations. To address these issues, we propose an efficient and accurate multi-scale detection framework based on YOLO11, specifically tailored for small UAVs in cluttered scenes. The framework incorporates three core modules: C3K2-FB to enhance feature extraction and suppress background noise, MS_FPN for edge-aware multi-scale feature fusion, and RFCBAM to jointly leverage spatial and channel attention for improved focus on discriminative regions. Extensive experiments on the DeTFly dataset show that our method achieves 94.4% mAP@0.5 and 60.9% mAP@0.5:0.95, outperforming state-of-the-art models including YOLO11n, YOLOv10n, and RT-DETR-L in both accuracy and robustness. These results validate the effectiveness of the proposed framework for real-world UAV detection tasks involving small-scale targets in cluttered environments.