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Journal of Sensors
Volume 2016 (2016), Article ID 8410731, 13 pages
Research Article

Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images

1Mechanical & Systems Engineering Department, Korea Military Academy, Seoul, Republic of Korea
2Mechanical Engineering Department, Virginia Tech, Blacksburg, VA 24060, USA

Received 26 March 2016; Revised 5 July 2016; Accepted 3 August 2016

Academic Editor: Juan A. Corrales

Copyright © 2016 Jong-Hwan Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.