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http://dx.doi.org/10.6109/jkiice.2018.22.12.1596

Real-Time Fire Detection based on CNN and Grad-CAM  

Kim, Young-Jin (Department of Computer Science & Engineering, Graduate School, Korea University of Technology and Education)
Kim, Eun-Gyung (School of Computer Science & Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.12, 2018 , pp. 1596-1603 More about this Journal
Abstract
Rapidly detecting and warning of fires is necessary for minimizing human injury and property damage. Generally, when fires occur, both the smoke and the flames are generated, so fire detection systems need to detect both the smoke and the flames. However, most fire detection systems only detect flames or smoke and have the disadvantage of slower processing speed due to additional preprocessing task. In this paper, we implemented a fire detection system which predicts the flames and the smoke at the same time by constructing a CNN model that supports multi-labeled classification. Also, the system can monitor the fire status in real time by using Grad-CAM which visualizes the position of classes based on the characteristics of CNN. Also, we tested our proposed system with 13 fire videos and got an average accuracy of 98.73% and 95.77% respectively for the flames and the smoke.
Keywords
Convolutional Neural Network; Grad-CAM; Inception ResNet V2; Image-based Fire Detection; Multi-labeled Classification;
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Times Cited By KSCI : 3  (Citation Analysis)
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