A machine vision approach to detect and categorize hydrocarbon fires in aircraft dry bays and engine compartments

Abstract

In this paper, a machine approach is applied to detect hydrocarbon fires in aircraft dry bays and engine compartments. The inputs to the machine vision system consist of a set of statistical measures derived from the histogram and image subtraction analyses of successive image frames. Specifically, heuristic rules based on the median, standard deviation and normalized first-order moment statistical measures of histogram data and the mean statistical measure of image subtraction data of successive frames are used to compute the likelihood of a fire event. This machine vision system is also tested for false alarms such as those due to flashlights and high-power halogen lights.