DFS-YOLO: A Dynamic Feature Collaboration and State Space Framework for UAV-Based Infrared Object Detection

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO’s performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance.