Small objects in UAV aerial images have low pixel proportions, are vulnerable to interference from complex backgrounds, and exhibit large scale variations, which often lead to missed detections and insufficient accuracy in existing methods. Therefore, an improved algorithm, YOLO11⁃RHO, is proposed for UAV aerial imagery based on YOLOv11. First, online convolutional re⁃parameterization (OREPA) with frequency⁃prior initialization is introduced into the backbone network to enhance the model's representation of high⁃frequency edge and texture information through spatial⁃frequency selectivity. Second, the RepNCSPELAN4_high module is used to optimize gradient propagation paths and construct a direct gradient pathway, thereby alleviating the attenuation of small⁃object features in deep network layers. Third, a hierarchical feature fusion block (HFFB) is integrated into the feature⁃fusion network. By combining a spatial⁃ and channel⁃decoupled adaptive attention mechanism, the block strengthens small⁃object representation and achieves multiscale information alignment. Finally, the overall architecture is designed for lightweight real⁃time deployment. Comparative and ablation experiments on the VisDrone2019 dataset show that the improved model achieves excellent performance, with mAP@0.5=0.401 and mAP@0.50∶0.95=0.245. It also maintains a lightweight architecture with 6.22 M parameters and 15.1 GFLOPs, effectively balancing high detection accuracy with real⁃time edge⁃device operation and satisfying practical end⁃side deployment requirements.