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.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference33 articles.
1. A Human Detection Approach for Burning Building Sites Using Deep Learning Techniques;Jaradat;Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE,2018
2. Standard on Thermal Imagers for the Fire Service,2020
3. Fire Sensing Technologies: A Review
4. Detection of Human Existence Using Thermal Imaging for Automated Fire Extinguisher;Aathithya,2019
5. Yolov4: Optimal speed and accuracy of object detection;Bochkovskiy;arXiv,2020
Cited by
22 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献