IV-PAFNet: Infrared-visible cross-modal alignment and fusion network for nighttime UAV object detection

Nighttime unmanned aerial vehicle object detection is challenged by degraded visible-light textures, cluttered infrared thermal responses, and local spatial discrepancies between infrared and visible images. To address these issues, we propose IV-PAFNet, an infrared-visible purification, alignment, and fusion network designed for object detection in nighttime and low-visibility conditions. IV-PAFNet consists of two primary components. The infrared signal purification and spatial alignment module applies learnable high-pass filtering and gated residual fusion to refine infrared responses, followed by visible-guided offset prediction and differentiable resampling to alleviate local cross-modal spatial discrepancies. The multi-scale fusion attention enhancement module performs channel-wise modality recalibration and spatial reliability weighting at multiple feature-pyramid levels, allowing the network to adapt the contribution of each modality to local imaging conditions. In the training-based DroneVehicle and generalization-based Camera-vehicle1 datasets, IV-PAFNet achieves 62.81% mAP@0.5:0.95 and 51.4% mAP@0.5:0.95, respectively. Additional controlled experiments investigate the distinct roles of infrared purification, visible-guided alignment, channel recalibration, and spatial reliability modeling, while statistical and computational analyses characterize run-to-run variation and inference cost. The code is available at https://github.com/iv-pafnet/IV-PAFNet.