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http://dx.doi.org/10.3837/tiis.2022.02.002

Efficient Forest Fire Detection using Rule-Based Multi-color Space and Correlation Coefficient for Application in Unmanned Aerial Vehicles  

Anh, Nguyen Duc (Department of Fire Engineering and Technology, University of Fire Prevention and Fighting)
Van Thanh, Pham (Department of Fire Engineering and Technology, University of Fire Prevention and Fighting)
Lap, Doan Tu (Fire Inspection Division, Vietnam Fire and Rescue Police Department)
Khai, Nguyen Tuan (Department of Electrical/Electronic and Computer Engineering, University of Ulsan)
Van An, Tran (Faculty of Basic Sciences and Foreign Languages, University of Fire Prevention and Fighting)
Tan, Tran Duc (Faculty of Electrical and Electronic Engineering, Phenikaa University)
An, Nguyen Huu (Department of Fire Engineering and Technology, University of Fire Prevention and Fighting)
Dinh, Dang Nhu (Department of Fire Engineering and Technology, University of Fire Prevention and Fighting)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.2, 2022 , pp. 381-404 More about this Journal
Abstract
Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.
Keywords
Forest Fire Detection; Rule-Based; RGB; YCbCr; Correlation Coefficient;
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Times Cited By KSCI : 3  (Citation Analysis)
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