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.

翻译：无人机图像中的小目标检测是一个长期存在的挑战，主要受限于低分辨率和背景杂波干扰。虽然融合RGB与红外数据提供了一种有前景的解决方案，但现有方法往往难以在有效的跨模态交互与计算效率之间取得平衡。本文提出MambaRefine-YOLO。其核心贡献包括：一个通过光照感知与差异感知门控机制自适应平衡RGB与红外模态的双门控互补Mamba融合模块，以及一个采用“先精炼后融合”策略以增强多尺度特征的层级特征聚合颈部。全面的实验验证了这种双管齐下的方法。在双模态DroneVehicle数据集上，完整模型实现了83.2%的最优平均精度，较基线提升7.9%。在单模态VisDrone数据集上，仅使用层级特征聚合颈部的变体同样表现出显著增益，证明了其普适性。本研究在精度与速度之间取得了优越的平衡，使其非常适用于实际无人机应用场景。