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Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images
Joint Authors
Kim, Jong-Hwan
Jo, Seongsik
Lattimer, Brian Y.
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Jong-Hwan& Jo, Seongsik& Lattimer, Brian Y.. 2016. Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images. Journal of Sensors،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1110648
Modern Language Association (MLA)
Kim, Jong-Hwan…[et al.]. Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images. Journal of Sensors No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1110648
American Medical Association (AMA)
Kim, Jong-Hwan& Jo, Seongsik& Lattimer, Brian Y.. Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1110648
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110648