Real-time probabilistic classification of fire and smoke using thermal imagery for intelligent firefighting robot

Abstract

A real-time probabilistic classification method was developed for identifying fire, smoke, their thermal reflections, and other objects in infrared images. This algorithm was formulated for use on a robot that will autonomously locate fires inside of a structure where the fire is outside the robot field of view. Thermal images were used to extract features due to the fact that long wavelength infrared is capable of imaging through zero visibility environments. For an autonomous navigation under fire environments, robots need to be able to differentiate between desired characteristics, such as fire and smoke, and those that may lead the robot in the incorrect direction, such as thermal reflections and other hot objects. The probabilistic classification method in this paper provides a robust, real-time algorithm that uses thermal images to classify fire and smoke with high accuracy. The algorithm is based on four statistical texture features identified through this work to characterize and classify the candidates. Based on classification of candidates from large-scale test data, the classification performance error was measured to be 6.8% based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5–99.9% for classifying fire and smoke using the test dataset.