@article{ref1,
title="Using deep learning with thermal imaging for human detection in heavy smoke scenarios",
journal="Sensors (Basel)",
year="2022",
author="Tsai, Pei-Fen and Liao, Chia-Hung and Yuan, Shyan-Ming",
volume="22",
number="14",
pages="e5351-e5351",
abstract="In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22145351",
url="http://dx.doi.org/10.3390/s22145351"
}