Citation

Tsai PF, Liao CH, Yuan SM. Sensors (Basel) 2022; 22(14): e5351.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s22145351

PMID

35891032

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.

Language: en

Keywords

convolutional neural network; evacuation in fire; firefighter protection; human detection; human rescue; infrared thermal camera; LWIR; real-time object detection; smoky fire scene; thermal imaging camera; YOLO