Volume 39, Issue 2, April 2023, Pages 742–750
Tidiane Fofana1, Sié OUATTARA2, and Alain Clement3
1 Laboratory of Signals and Electrical Systems (L2SE)), Institut National Polytechnique Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
2 RMI Electricité et électricité appliquées, Institut National Polytechnique Felix Houphouët-Boigny (INP-HB), Côte d’Ivoire
3 LARIS, SFR MATHSTIC, Université d’Angers, Angers, France
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Work in the field of fire and smoke detection is becoming an increasingly covered subject. Conventional algorithms use exclusively models based on feature vectors. These vectors are difficult to define and depend largely on the type of fire being treated. These traditional methods give results with low detection rates and high false classification rates. The current trend is to take an innovative approach to solving this problem by using an algorithm to automatically determine useful features to classify fire and smoke. In this paper, we propose a convolutional neural network to identify fire and smoke from real-time images. Convolutional neural networks have shown their great performance in the field of object classification. Tested on real image sequences, the proposed approach achieves better classification performance than conventional methods. These results clearly indicate that the use of convolutional neural networks for fire detection is very encouraging.
Author Keywords: Fire, Smoke, classification, dropout, convolutional neural network.
Tidiane Fofana1, Sié OUATTARA2, and Alain Clement3
1 Laboratory of Signals and Electrical Systems (L2SE)), Institut National Polytechnique Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
2 RMI Electricité et électricité appliquées, Institut National Polytechnique Felix Houphouët-Boigny (INP-HB), Côte d’Ivoire
3 LARIS, SFR MATHSTIC, Université d’Angers, Angers, France
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Work in the field of fire and smoke detection is becoming an increasingly covered subject. Conventional algorithms use exclusively models based on feature vectors. These vectors are difficult to define and depend largely on the type of fire being treated. These traditional methods give results with low detection rates and high false classification rates. The current trend is to take an innovative approach to solving this problem by using an algorithm to automatically determine useful features to classify fire and smoke. In this paper, we propose a convolutional neural network to identify fire and smoke from real-time images. Convolutional neural networks have shown their great performance in the field of object classification. Tested on real image sequences, the proposed approach achieves better classification performance than conventional methods. These results clearly indicate that the use of convolutional neural networks for fire detection is very encouraging.
Author Keywords: Fire, Smoke, classification, dropout, convolutional neural network.
How to Cite this Article
Tidiane Fofana, Sié OUATTARA, and Alain Clement, “Smoke and fire detection by a convolutional neural network based on a combinatorial model,” International Journal of Innovation and Applied Studies, vol. 39, no. 2, pp. 742–750, April 2023.
