Back to articles
Articles
Volume: 30 | Article ID: art00004
Image
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks
  DOI :  10.2352/ISSN.2470-1173.2018.09.SRV-127  Published OnlineJanuary 2018
Abstract

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. Using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.

Subject Areas :
Views 16
Downloads 1
 articleview.views 16
 articleview.downloads 1
  Cite this article 

Kyongsik Yun, Jessi Bustos, Thomas Lu, "Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2018,  pp 127-1 - 127-4,  https://doi.org/10.2352/ISSN.2470-1173.2018.09.SRV-127

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2018
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology