A victim risk identification model for nature‐induced urban disaster emergency response

Abstract

In recent years, nature‐induced urban disasters in high‐density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real‐time post‐disaster situation made it difficult for the decision‐makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real‐time performance and accuracy of on‐site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7‐W6 (YOLOv7‐W6) algorithm. This model defines the “fall‐down” pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1 ms) on the established disaster victim database compared to other state‐of‐the‐art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on‐site victim risk identification approach, contributing significantly to government emergency decision‐making and response.