RVGM-YOLO: A Hierarchical State Enhancement Network for UAV Infrared Target Detection

Abstract

UAV infrared target detection in military reconnaissance and disaster response faces significant challenges, including low-contrast imagery with sparse textures, scale variations under dynamic flight perspectives, and occlusion-induced detection degradation. To address these issues, we propose RVGM-YOLO, a Hierarchical State Enhancement Network based on YOLOv11. The framework integrates three key innovations: a state-recursive spatial context module (SRSCM) enhances local features in shallow layers via recursive structures and strengthens global context in deep layers using state-space models; Haar wavelet downsampling (HWD) preserves small-target edge contours and texture details through multiresolution analysis; and a multiscale SEAM detection head (MultiSEAM) leverages multihead attention for robustness under occlusion. Experiments on HIT-UAV demonstrate RVGM-YOLO achieves 91.3% precision, 86.7% recall, 88.5% $F1$ -score, and 62.0% mAP@50:95, outperforming baselines while maintaining real-time efficiency. The code is available at: https://github.com/CarlWang-13/RVGM-YOLO