Dynamic fire and smoke detection and classification for flashover prediction

Alexander Huyen; Kyongsik Yun; Sky De Baun; Samuel Wiggins; Jessi Bustos; Thomas Lu; Edward Chow

doi:10.1117/12.2588175

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12 April 2021 Dynamic fire and smoke detection and classification for flashover prediction

Alexander Huyen, Kyongsik Yun, Sky De Baun, Samuel Wiggins, Jessi Bustos, Thomas Lu, Edward Chow

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Proceedings Volume 11735, Pattern Recognition and Tracking XXXII; 1173502 (2021) https://doi.org/10.1117/12.2588175
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Flashover is a dangerous phenomenon caused by near-simultaneous ignition of exposed materials. It is one of the major causes of firefighter fatalities. Research has been done using CMOS vision cameras combined with thermal sensors to perform remote detection and dynamic classification of fire and smoke patterns. Tests and experiments have been done to detect fire and smoke remotely. The inexpensive visible and infrared sensors used in the tests corroborate and closely follow the detailed trends recorded by the more expensive (and less mobile) radiometers and thermocouples. Deep neural networks (DNN) have been used to detect, classify and track fire and smoke areas. Real-time segmentation is utilized to measure the fire and smoke boundaries. The segmentations are used to dynamically monitor fluctuations in temperature, fire size and smoke progression in the monitored areas. A fire and smoke progression curve has been drawn to predict the flashover point. In the paper, data analysis and preliminary results will be shown.

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Alexander Huyen, Kyongsik Yun, Sky De Baun, Samuel Wiggins, Jessi Bustos, Thomas Lu, and Edward Chow "Dynamic fire and smoke detection and classification for flashover prediction", Proc. SPIE 11735, Pattern Recognition and Tracking XXXII, 1173502 (12 April 2021); https://doi.org/10.1117/12.2588175

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