LF-DETR: A Laplacian Frequency Enhanced DETR for Aerial RGB-Infrared Pedestrian Detection

Pedestrian detection from unmanned aerial vehicles (UAVs) holds significant value in security surveillance and emergency response applications. While visible-infrared (RGB-IR) fusion technology demonstrates potential in handling complex lighting conditions through cross-modal information complementarity, current mainstream fusion mechanisms still suffer from two evident shortcomings: (1) Existing approaches insufficiently account for the significant differences in noise distribution between infrared and visible images under varying imaging conditions, leading to unstable feature representations and posing fundamental challenges to subsequent effective fusion; and (2) Existing fusion strategies lack dynamic adaptability to features from different modalities, making it difficult to fully exploit complementary key information across modalities. To address these issues, this paper proposes a novel Laplacian Frequency Enhanced DETR (LF-DETR). The core innovations are threefold: (1) A Laplacian of Gaussian feature enhancement module is designed to independently enhance features in the visible and infrared branches at the early stage of feature extraction, effectively improving the representation quality of each modality. (2) A learnable frequency-domain fusion module is constructed to achieve adaptive complementary fusion of cross-modal features. (3) A dual-domain collaborative framework is proposed to integrate the above modules within a unified DETR architecture for RGB-IR pedestrian detection. Experimental results on the public RGBTDronePerson, VTUAV-det and DVTOD datasets demonstrate that LF-DETR achieves state-of-the-art performance, with particularly significant detection gains in challenging scenarios such as nighttime and low-light conditions, validating the effectiveness and superiority of the proposed method.