DeepSafe:Two-level deep learning approach for disaster victims detection  

作　　者：Amir AZIZI Panayiotis CHARALAMBOUS Yiorgos CHRYSANTHOU 

机构地区：[1]CYENS Centre of Excellence,Nicosia 1016,Cyprus [2]Department of Computer Science,University of Cyprus,Nicosia 1678,Cyprus

出　　处：《虚拟现实与智能硬件(中英文)》2025年第2期139-154,共16页Virtual Reality & Intelligent Hardware

基　　金：Supported by European Union’s Horizon 2020 Research and Innovation Program(739578);the Government of the Republic of Cyprus through the Deputy Ministry of Research,Innovation,and Digital Policy.

摘　　要：Background Efficient disaster victim detection(DVD)in urban areas after natural disasters is crucial for minimizing losses.However,conventional search and rescue(SAR)methods often experience delays,which can hinder the timely detection of victims.SAR teams face various challenges,including limited access to debris and collapsed structures,safety risks due to unstable conditions,and disrupted communication networks.Methods In this paper,we present DeepSafe,a novel two-level deep learning approach for multilevel classification and object detection using a simulated disaster victim dataset.DeepSafe first employs YOLOv8 to classify images into victim and non-victim categories.Subsequently,Detectron2 is used to precisely locate and outline the victims.Results Experimental results demonstrate the promising performance of DeepSafe in both victim classification and detection.The model effectively identified and located victims under the challenging conditions presented in the dataset.Conclusion DeepSafe offers a practical tool for real-time disaster management and SAR operations,significantly improving conventional methods by reducing delays and enhancing victim detection accuracy in disaster-stricken urban areas.

关 键 词：Victims detection Deep learning Disaster management YOLO Victims identification 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]