Small Object Detection in UAV Imagery via Multimodal RGB-Thermal Fusion

Aerial surveillance using Unmanned Aerial Vehicles (UAVs) faces challenges in detecting small targets, particularly under varying illumination conditions. Single-modality detectors often degrade in performance when targets are visually camouflaged or when lighting is poor. To improve robustness across day/night and reduced-visibility scenarios, this paper introduces multimodal RGB-thermal perception, leveraging the complementary information provided by visible-spectrum and thermal infrared imagery. An enhanced multimodal architecture based on DEYOLO (Dual-Feature-Enhancement YOLO) is proposed, specifically optimized for small object detection in medium-high altitude imagery. The model employs dual RGB and thermal backbones and integrates lightweight architectural refinements aimed at improving multi-scale representation and cross-modal alignment. In particular, the feature pyramid is extended toward higher-resolution levels to better capture small targets, with SPDConv introduced as an additional module in the backbone. The neck is enhanced with SPANet to improve the extraction of fine-grained details, while attention-based fusion modules adaptively weight spatial and channel information across modalities. The approach is evaluated on both a public RGB-thermal UAV dataset and a custom dual-sensor aerial dataset. Experimental results show that the proposed multimodal configuration achieves a 78% mAP50, improving detection accuracy and robustness compared to single-modality baselines, especially in low-light and cluttered environments. Ablation studies further confirm that each architectural component contributes complementary improvements, especially in enhancing multi-scale representation and cross-modal feature fusion.