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http://dx.doi.org/10.6109/jkiice.2016.20.9.1649

Image based Fire Detection using Convolutional Neural Network  

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.20, no.9, 2016 , pp. 1649-1656 More about this Journal
Abstract
Performance of the existing sensor-based fire detection system is limited according to factors in the environment surrounding the sensor. A number of image-based fire detection systems were introduced in order to solve these problem. But such a system can generate a false alarm for objects similar in appearance to fire due to algorithm that directly defines the characteristics of a flame. Also fir detection systems using movement between video flames cannot operate correctly as intended in an environment in which the network is unstable. In this paper, we propose an image-based fire detection method using CNN (Convolutional Neural Network). In this method, firstly we extract fire candidate region using color information from video frame input and then detect fire using trained CNN. Also, we show that the performance is significantly improved compared to the detection rate and missing rate found in previous studies.
Keywords
Artificial Intelligence; Convolutional Neural Network; Deep Learning; Image-based fire detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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