Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks

TL;DR

This paper presents a method using conditional generative adversarial networks to enhance firefighter video footage, enabling early prediction of flashovers up to 55 seconds in advance, thereby improving firefighter safety.

Contribution

It introduces a novel application of GANs for real-time fire scene enhancement and flashover prediction from body camera videos, a new approach in fire safety technology.

Findings

Predicted flashovers up to 55 seconds before occurrence.

Enhanced dark fire and smoke patterns in videos.

Demonstrated potential for real-time fire hazard monitoring.

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.