Application of Artificial Neural Networks to Machine Vision Flame Detection

The U.S. Air Force has identified a need for rapid, accurate and reliable detection and classification of fires. To address this need, a proof- of-concept neural network-based, intelligent machine vision interface for the detection of flame signatures in the visible spectrum has been developed. The objective of the work conducted under this Phase I program has been to determine the feasibility of using machine vision techniques and neural network computation to detect and classify visible spectrum signatures of fire in the presence of complex background imagery. Standard fire detectors which rely on heat or smoke sensing devices tend to be slow and to react only after the fire reaches a significant level. Current electromagnetic sensing techniques have the desired speed but lack accuracy. The Phase I program approach to these problems used machine vision techniques to generate digitally filtered HSI Hue, Saturation, Intensity-formatted video data. Once filtered, these data were then presented to an artificial neural network for analysis.