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

In this paper, a machine vision approach is applied to detect and categorize hydrocarbon fires in aircraft dry bays and engine compartments. Images for computer analysis are provided by charge-coupled device imaging sensors placed inside dry bays and engine compartments. Using a set of heuristics based on statistical measures derived from the histogram and image subtraction analyses of successive image frames, we showed that it is possible to detect and categorize life-threatening fires from nonfire/nonlethal events accurately in submillisecond response time. Specifically, the median, standard deviation, and 1st-order moment statistical measures of the histogram data of each image frame are used to confirm the presence or absence of fire. Concurrently, another set of mean, median, and standard deviation statistical measures from the image subtraction of two successive frames are used to determine the growth and subsequently reaffirm the existence of a fire. This approach is also tested for false alarms such as those due to flashlights and high-power halogen lights