Citation

Kustu T, Taskin A. Int. J. Disaster Risk Reduct. 2023; 96: e103906.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2023.103906

PMID

unavailable

Abstract

Cities, which are getting crowded day by day, face a number of issues, including increased planning, inadequate infrastructure, heavy traffic and security. Due to such issues, the cities are more susceptible to natural disasters. Although many preparations are made against natural disasters in urban life, it is evident that adequate measures are not taken against secondary disasters. One of the most destructive and frequent secondary disasters is fires after earthquakes. In this study, an early fire detection system is proposed in order to minimize the losses caused by fires in the potentially chaotic environment that may occur after earthquakes in cities. This system consists of a structure that detects fire with convolutional neural network (CNN) based You Only Look Once (YOLO) model, determines the geolocation of the fire with stereo vision/epipolar geometry and provides information to the disaster management center via wireless sensor network (WSN). Experimental test findings conducted in İstanbul verify that the proposed system would be useful for post-earthquake fire detection with low cost, high reliability and high accuracy.

Language: en

Keywords

Disaster management; Post-earthquake fire geolocation; Secondary disaster; Smart cities; Stereo vision-based detection