MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery

• URL: http://arxiv.org/abs/2511.19134v1
• Date: Mon, 24 Nov 2025 13:59:01 GMT
• Title: MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery
• Authors: Shuyu Cao, Minxin Chen, Yucheng Song, Zhaozhong Chen, Xinyou Zhang,
• Abstract summary: Small object detection in Unmanned Aerial Vehicle (UAV) imagery is a persistent challenge, hindered by low resolution and background clutter.<n>We introduce Mamba-YOLO, a fusion module that balances RGB and IR modalities through illumination-aware and difference-aware gating mechanisms.<n>Our work presents a superior balance between accuracy and speed, making it highly suitable for real-world UAV applications.
• Score: 1.005854289245731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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