Work

UFD-YOLO: a lightweight multi-reference model for urban flood risk level detection from surveillance images

Year: 2026

Type: article

Abstract: Flood disasters pose persistent challenges to urban safety and emergency management. Traditional remote sensing technologies remain limited in real-time capability and spatial coverage. In surveillance-based urban flood monitoring, direct physical measurement of flood depth is often unavailable; therefore, flood risk level detection based on visual reference objects provides a feasible alternative for rapid flood assessment. However, existing lightweight YOLO models still struggle to accurately identify submerged targets under partial submergence, large-scale variation, and complex urban conditions. To address these issues, this study proposes UFD-YOLO, a lightweight framework for urban flood risk level detection using pedestrians and vehicles as multi-scale visual references. First, a reference-based flood risk level criterion was established according to the submergence states of pedestrians and vehicles, and a dataset covering diverse urban scenarios was constructed. Second, the DySnakeConv, AIFI, and DetectAux were progressively integrated into YOLO11n to improve local structural perception, strengthen feature interaction, and enhance multi-scale supervisory learning. Compared with YOLO11n, UFD-YOLO improves Precision, Recall, mAP50, and mAP50:95 by 2.5%, 5.1%, 5.8%, and 2.6%, respectively, while maintaining low computational cost. Additional evaluations further demonstrate its accurate and stable performance in challenging scenarios. Overall, this study provides an effective technical approach for urban flood risk assessment.

Source: npj natural hazards.

DOI: 10.1038/s44304-026-00224-5