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’sAP50reached 69.7%, 57.1%, 95.4% and 89.8%, respectively, surpassing the latest SOTA methods, and itsAPSalso 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.