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A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network

Graduate School of Disaster Prevention, Kangwon National University, Samcheok 25913, Korea
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Academic Editor: James A. Lutz
Fire 2022, 5(4), 108; https://doi.org/10.3390/fire5040108
Received: 24 June 2022 / Revised: 21 July 2022 / Accepted: 26 July 2022 / Published: 27 July 2022
(This article belongs to the Section Fire Science Models, Remote Sensing, and Data)
This study sought an effective detection method not only for flame but also for the smoke generated in the event of a fire. To this end, the flame region was pre-processed using the color conversion and corner detection method, and the smoke region could be detected using the dark channel prior and optical flow. This eliminates unnecessary background regions and allows selection of fire-related regions. Where there was a pre-processed region of interest, inference was conducted using a deep-learning-based convolutional neural network (CNN) to accurately determine whether it was a flame or smoke. Through this approach, the detection accuracy is improved by 5.5% for flame and 6% for smoke compared to when a fire is detected through the object detection model without separate pre-processing. View Full-Text
Keywords: fire safety system; computer vision; image processing; deep learning fire safety system; computer vision; image processing; deep learning
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MDPI and ACS Style

Ryu, J.; Kwak, D. A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network. Fire 2022, 5, 108. https://doi.org/10.3390/fire5040108

AMA Style

Ryu J, Kwak D. A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network. Fire. 2022; 5(4):108. https://doi.org/10.3390/fire5040108

Chicago/Turabian Style

Ryu, Jinkyu, and Dongkurl Kwak. 2022. "A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network" Fire 5, no. 4: 108. https://doi.org/10.3390/fire5040108

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