Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks
This addresses a critical safety issue for firefighters by providing early warning of flashovers, though it is incremental as it builds on existing GAN methods for image enhancement.
The paper tackled the problem of predicting flashovers in fires by using conditional generative adversarial networks to enhance dark fire and smoke patterns from firefighters' body camera videos, achieving a prediction time of 55 seconds before occurrence in preliminary tests.
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.