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A new PSO-based approach to fire flame detection using K-Medoids clustering

Version 2 2024-06-06, 08:07
Version 1 2016-11-30, 15:17
journal contribution
posted on 2024-06-06, 08:07 authored by A Khatami, S Mirghasemi, Abbas KhosraviAbbas Khosravi, Chee Peng LimChee Peng Lim, S Nahavandi
Automated computer vision-based fire detection has gained popularity in recent years, as every fire detection needs to be fast and accurate. In this paper, a new fire detection method using image processing techniques is proposed. We explore how to create a fire flame-based colour space via a linear multiplication of a conversion matrix and colour features of a sample image. We show how the matrix multiplication can result in a differentiating colour space, in which the fire part is highlighted and the non-fire part is dimmed. Particle Swarm Optimization (PSO) and sample pixels from an image are used to obtain the weights of the colour-differentiating conversion matrix, and K-medoids provides a fitness metric for the PSO procedure. The obtained conversion matrix can be used for fire detection on different fire images without performing the PSO procedure. This allows a fast and easy implementable fire detection system. The empirical results indicate that the proposed method provides both qualitatively and quantitatively better results when compared to some of the conventional and state-of-the-art algorithms.

History

Journal

Expert systems with applications

Volume

68

Pagination

69-80

Location

Amsterdam, The Netherlands

ISSN

0957-4174

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2016, Elsevier

Publisher

Elsevier

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