Reflectance-Guided Progressive Feature Alignment Network for All-Day UAV Object Detection

Object detection using visible-infrared images has become increasingly crucial for all-day applications of unmanned aerial vehicle (UAV). However, existing multi-modal detection methods face significant challenges in low-light conditions, where degraded visible image quality exacerbates weak alignment issues and compromises feature fusion effectiveness. Although recent approaches have attempted to address these issues through cross-attention mechanisms or feature alignment strategies, they often suffer from unstable performance and limited generalization capability in challenging nighttime scenarios. To address these limitations, we propose a novel Reflectance-Guided Progressive Feature Alignment Network (RGFNet) for robust UAV object detection. Our proposed method leverages the illumination-invariant characteristic of reflectance features decomposed from visible images via Retinex theory to guide cross-modal alignment and fusion. Specifically, we design a Reflectance-Guided Collaborative Alignment Module (RCAM) that utilizes reflectance guidance to perform bidirectional feature alignment between visible and infrared modalities, effectively reducing position misalignment under varying lighting conditions. Furthermore, we introduce a Light-Aware Selective Fusion Module (LSFM) that maps multi-modal features into a shared hidden state space through selective state space mechanism, enabling efficient feature interaction while maintaining linear computational complexity. Extensive experiments on two challenging UAV detection benchmarks, DroneVehicle and DVTOD, demonstrate the superiority of our method. RGFNet achieves state-of-the-art performance with 81.4% mAP on DroneVehicle and 88.5% mAP on DVTOD. The code is available at https://github.com/uavdet/RGFNet.