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

TL;DR

MambaRefine-YOLO introduces a dual-modality small object detection framework for UAV imagery that effectively fuses RGB and IR data using adaptive gating and hierarchical feature aggregation, achieving state-of-the-art accuracy with efficiency.

Contribution

The paper proposes the DGC-MFM and HFAN modules for improved cross-modal fusion and multi-scale feature enhancement in UAV small object detection.

Findings

Achieves 83.2% mAP on DroneVehicle dataset, outperforming baselines.

Demonstrates significant accuracy gains on VisDrone with a single-modality variant.

Balances detection accuracy and computational speed for real-world UAV use.

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.