  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.
