RSRT-DETR: Hierarchical Polar Attention and Multiscale Hypergraph Networks for Dense Remote Sensing Small-Object Detection

Recent advances in optical remote-sensing have expanded the use of unmanned aerial vehicles (UAV), yet accurately localising dense, small objects in low-resolution UAV imagery remains challenging. We propose RSRT-DETR, a lightweight DETR derivative tailored for UAV-borne remote-sensing that jointly enhances global context modelling and fine-grained detail preservation. RSRT-DETR couples a Polar Dynamic Spatial Fine-tuning (PDSF) Attention module with a lightweight backbone to amplify fine-grained geometry while suppressing background noise, and embeds a Multi-Scale Hypergraph Feature Network (MS-HFNet) that reasons over high-order spatial relations to disambiguate cluttered targets. These features are hierarchically fused by the HyperSemantic Fusion Network (HSFN), whose progressive edge-enhancement, resolution-adaptation, and multi-granularity aggregation schemes jointly preserve cross-scale context without sacrificing speed. Evaluated on three challenging benchmarks—DOTAv1.0, VisDrone2019, NWPU VHR-10 and DIOR, RSRT-DETR’s AP₅₀ reached 69.7%, 57.1%, 95.4% and 89.8%, respectively, surpassing the latest SOTA methods, and its AP_S also achieved the highest value. Meanwhile, the number of parameters and FLOPs are lower than those of similar DETR variants. The above results prove that RSRT-DETR achieves the optimal coordination of detection accuracy, model scale, and real-time performance in complex backgrounds and multi-scale dense small target scenes, providing a new paradigm for building efficient and reliable UAV remote sensing target detection systems. Our code is avaliable on: https://github.com/zhy1109/RSRT-DETR.