An efficient hybrid algorithm for fire flame detection

Khatami, Amin, Mirghasemi, Saeed, Khosravi, Abbas and Nahavandi, Saeid 2015, An efficient hybrid algorithm for fire flame detection, in IJCNN 2015: Proceedings of the 2015 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/IJCNN.2015.7280590.

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Title An efficient hybrid algorithm for fire flame detection
Author(s) Khatami, Amin
Mirghasemi, Saeed
Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Conference name International Joint Conference on Neural Networks (2015: Killarney, Ireland)
Conference location Killarney, Ireland
Conference dates 12-17 Jul. 2015
Title of proceedings IJCNN 2015: Proceedings of the 2015 International Joint Conference on Neural Networks
Publication date 2015
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3∗3 matrix to a new color space. In the next step, fuzzy c-mean clustering method (FCM) is used to distinguish between fire flame and non-fire flame pixels. Particle Swarm Optimization algorithm (PSO) is also utilized in the last step to decrease the error value measured by FCM after conversion. Finally, we apply Otsu threshold method to the new converted images to make a binary picture. Empirical results show the strength, accuracy and fast-response of the proposed algorithm in detecting fire flames in color images.
ISBN 9781479919604
Language eng
DOI 10.1109/IJCNN.2015.7280590
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
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