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JDR Vol.19 No.1 pp. 204-213
(2024)
doi: 10.20965/jdr.2024.p0204

Paper:

Intelligent System Detection of Dead Victims at Natural Disaster Areas Using Deep Learning

Moch. Zen Samsono Hadi ORCID Icon, Prima Kristalina ORCID Icon, Aries Pratiarso, M. Helmi Fauzan, and Roycardo Nababan

Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya
Jl. Raya ITS, PENS Campus, Sukolilo, Surabaya, East Java 60111, Indonesia

Corresponding author

Received:
July 21, 2023
Accepted:
October 24, 2023
Published:
February 1, 2024
Keywords:
volcanic disaster, drone, camera, deep learning
Abstract

Disaster is the occurrence or sequence of occurrences that endangers and disrupts people’s lives and livelihoods due to natural and/or non-natural as well as human elements, including fatalities, property loss, environmental harm, and psychological effects. In addition to concentrating on the victims’ safety and their own safety, the search and rescue (SAR) team plays a significant part in this evacuation operation. Based on these issues, this study examined how to use a drone equipped with electronic equipment to search for victims on the ground to speed up the evacuation process at natural disaster sites, assisting the evacuation process and enhancing the safety of the SAR team. The drone carries a near-infrared camera and GPS. The images captured by the camera provide the parameters for classifying victims using deep learning. The system has been implemented by sampling data from human poses resembling the position of the victims’ bodies from natural disasters. From the experimental results, the system can detect objects with high accuracy, that is, 99% in both static and dynamic conditions. The best model results were obtained at a height of 2 meters with a low error percentage.

Cite this article as:
M. Hadi, P. Kristalina, A. Pratiarso, M. Fauzan, and R. Nababan, “Intelligent System Detection of Dead Victims at Natural Disaster Areas Using Deep Learning,” J. Disaster Res., Vol.19 No.1, pp. 204-213, 2024.
Data files:
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Last updated on Jul. 19, 2024