Abstract
Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified.
Highlights
Intelligent firefighting humanoid robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks [1,2,3,4,5]
(i) RGB camera cannot function in smoke-filled environments [2, 14] (i) Can be influenced by dynamical robot motion (ii) Expensive computation for motion compensation (i) The higher the order texture features, the more the computation (i) Slow learning speed (ii) GPUs required due to expensive computation to aid in firefighting tasks within smoke-filled environments [20,21,22] as well as fire-front and burned-area recognition in remote sensing [23], are used in this research
There are several classification methods commonly used in supervised machine learning; k-nearest neighbors, decision tree (DT), neural networks (NN), support vector machine (SVM), and Naıve Bayesian
Summary
Intelligent firefighting humanoid robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks [1,2,3,4,5]. One task is locating a fire inside of a structure outside the robot field of view (FOV). Fire, smoke, and their thermal reflections can be clues to determine a heading that will lead the robot to the fire so that it can suppress it. Research for accurately classifying these clues has been incomplete
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