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

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.

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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,

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Copyright © Society for Imaging Science and Technology 2018
Electronic Imaging
Society for Imaging Science and Technology