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Real-Time Classification of Water Spray and Leaks for Robotic Firefighting

Real-Time Classification of Water Spray and Leaks for Robotic Firefighting

Joshua G. McNeil, Brian Y. Lattimer
Copyright: © 2015 |Volume: 5 |Issue: 1 |Pages: 26
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522505945|DOI: 10.4018/IJCVIP.2015010101
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MLA

McNeil, Joshua G., and Brian Y. Lattimer. "Real-Time Classification of Water Spray and Leaks for Robotic Firefighting." IJCVIP vol.5, no.1 2015: pp.1-26. http://doi.org/10.4018/IJCVIP.2015010101

APA

McNeil, J. G. & Lattimer, B. Y. (2015). Real-Time Classification of Water Spray and Leaks for Robotic Firefighting. International Journal of Computer Vision and Image Processing (IJCVIP), 5(1), 1-26. http://doi.org/10.4018/IJCVIP.2015010101

Chicago

McNeil, Joshua G., and Brian Y. Lattimer. "Real-Time Classification of Water Spray and Leaks for Robotic Firefighting," International Journal of Computer Vision and Image Processing (IJCVIP) 5, no.1: 1-26. http://doi.org/10.4018/IJCVIP.2015010101

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Abstract

Robotic firefighting is an area of increased focus as a way of limiting the exposure of firefighters to hazardous environments. A suppression system must incorporate multiple functionalities to allow for closed-loop firefighting control. One area of development is classifying water spray as a way of correcting errors between suppressant placement and fire location. An IR vision system is presented which is capable of identifying water. Image segmentation is performed, followed by a process that classifies regions of interest as water or non-water objects. A probabilistic classification method, using Naïve Bayes classifier, was applied on a varied dataset of differing water temperatures and sprays. Objects were segmented using frame differencing with image intensity and difference thresholds. Segments were manually labeled to create a training dataset. Precision, recall, F-measure, and G-measure results of the classifier on a separate test dataset ranged from 86.1-97.4% for classifying water objects using the test dataset with water classification alone having 94.2-97.4% accuracy.

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