MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914168883380224 |
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| author | Cao, Shuyu Chen, Minxin Song, Yucheng Chen, Zhaozhong Zhang, Xinyou |
| author_facet | Cao, Shuyu Chen, Minxin Song, Yucheng Chen, Zhaozhong Zhang, Xinyou |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19134 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery Cao, Shuyu Chen, Minxin Song, Yucheng Chen, Zhaozhong Zhang, Xinyou Computer Vision and Pattern Recognition 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. |
| title | MambaRefine-YOLO: A Dual-Modality Small Object Detector for UAV Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.19134 |