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Predicting Rapid Fire Growth (Flashover) Using Generative Adversarial Networks

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dc.contributor.author Yun, Kyongsik
dc.contributor.author Bustos, Jessi
dc.contributor.author Lu, Thomas
dc.date.accessioned 2020-04-22T02:55:47Z
dc.date.available 2020-04-22T02:55:47Z
dc.date.issued 2018-01-28
dc.identifier.citation The Society for Imaging Science and Technology (IS&T) International Symposium on Electronic Imaging, Burlingame, California, January 28 - February 2, 2018 en_US
dc.identifier.clearanceno 18-0535
dc.identifier.uri http://hdl.handle.net/2014/48038
dc.description.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. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2018 en_US
dc.subject flashover prediction en_US
dc.subject generative adversarial networks en_US
dc.subject object recognition and segmentation en_US
dc.subject computer vision en_US
dc.title Predicting Rapid Fire Growth (Flashover) Using Generative Adversarial Networks en_US
dc.type Preprint en_US


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