{"ID":2837575,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19134","arxiv_id":"2511.19134","title":"MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery","abstract":"Small object detection in Unmanned Aerial Vehicle (UAV) imagery is a persistent challenge, hindered by low resolution and background clutter. While fusing RGB and infrared (IR) data offers a promising solution, existing methods often struggle with the trade-off between effective cross-modal interaction and computational efficiency. In this letter, we introduce MambaRefine-YOLO. Its core contributions are a Dual-Gated Complementary Mamba fusion module (DGC-MFM) that adaptively balances RGB and IR modalities through illumination-aware and difference-aware gating mechanisms, and a Hierarchical Feature Aggregation Neck (HFAN) that uses a ``refine-then-fuse'' strategy to enhance multi-scale features. Our comprehensive experiments validate this dual-pronged approach. On the dual-modality DroneVehicle dataset, the full model achieves a state-of-the-art mAP of 83.2%, an improvement of 7.9% over the baseline. On the single-modality VisDrone dataset, a variant using only the HFAN also shows significant gains, demonstrating its general applicability. Our work presents a superior balance between accuracy and speed, making it highly suitable for real-world UAV applications.","short_abstract":"Small object detection in Unmanned Aerial Vehicle (UAV) imagery is a persistent challenge, hindered by low resolution and background clutter. While fusing RGB and infrared (IR) data offers a promising solution, existing methods often struggle with the trade-off between effective cross-modal interaction and computationa...","url_abs":"https://arxiv.org/abs/2511.19134","url_pdf":"https://arxiv.org/pdf/2511.19134v1","authors":"[\"Shuyu Cao\",\"Minxin Chen\",\"Yucheng Song\",\"Zhaozhong Chen\",\"Xinyou Zhang\"]","published":"2025-11-24T13:59:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}