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 uncrewed aerial vehicles (UAVs). However, existing multimodal 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 multimodal features into a shared hidden state space through a selective state-space mechanism, enabling efficient feature interaction while maintaining linear computational complexity. Extensive experiments on two challenging UAV detection benchmarks, DroneVehicle and drone-based visible-thermal object detection (DVTOD), demonstrate the superiority of our method. The RGFNet achieves state-of-the-art performance with 81.4% mean average precision (mAP) on DroneVehicle and 88.5% mAP on DVTOD. The code is available at https://github.com/uavdet/RGFNet