BDRNet: Background-Aware Dynamic-Scale Routing Network for UAV Remote Sensing Object Detection

Object detection in UAV remote sensing imagery remains challenging due to severe scale variation, dense object distributions, complex background clutter, and localization ambiguity caused by extremely small objects. To address these issues, this paper proposes BDRNet, a lightweight background-aware dynamic-scale routing network for UAV remote sensing object detection. First, a background-aware feature enhancement (BAFE) module is introduced into the backbone to enhance feature representation through horizontal and vertical contextual modeling, improving target-related responses in complex aerial scenes. Second, a dynamic-scale routing pyramid (DSRP) is designed to retain the high-resolution P2 branch and adaptively integrate multi-scale features through spatially dynamic routing, alleviating the loss of fine-grained information and improving the representation of small and scale-varied objects. Third, a scale- and geometry-aware normalized Wasserstein distance (SGNW) loss is proposed by modeling bounding boxes as two-dimensional Gaussian distributions. By incorporating aspect-ratio-guided geometric weighting and scale-aware dynamic fusion, SGNW improves regression stability for small objects while preserving geometric constraints for medium and large targets. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that BDRNet consistently improves detection accuracy over the YOLOv10s detector while maintaining a comparable model size and computational cost. Compared with several mainstream lightweight detectors, BDRNet achieves a favorable accuracy–efficiency trade-off, demonstrating its effectiveness for UAV remote sensing object detection in complex aerial scenarios.