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Efficient Flame Detection Based on Static and Dynamic Texture Analysis in Forest Fire Detection

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Abstract

Flame detection is a specialized task in fire detection and forest fire monitoring systems. In this paper, a static and dynamic texture analysis of flame in forest fire detection is proposed. The flames are initially segmented, based on the color in YCbCr (luminance, chrominance blue and chrominance red components) color space called candidate flame region. From the candidate flame region, the static and dynamic texture features are extracted. Static texture features are obtained by hybrid texture descriptors. Dynamic texture features are derived using 2D wavelet decomposition in temporal domain and 3D volumetric wavelet decomposition. Finally, extreme learning machine classifier is used to classify the candidate flame region as real flame or non-flame, based on the extracted texture features. The proposed flame detection system is applied to various fire detection scenes, in the real environments and it effectively distinguishes fire from fire-colored moving objects. The results show that the proposed fire detection technique achieves the average detection rate of 95.65% which is better compared to other state-of-art methods.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Tamil Nadu, 629157, India

    C. Emmy Prema

  2. Department of Electronics and Communication Engineering, Lourdes Mount College of Engineering and Technology, Marthandam, Tamil Nadu, 629 195, India

    S. S. Vinsley

  3. Department of Mechanical Engineering, University College of Engineering, Nagercoil, Tamilnadu, 629 004, India

    S. Suresh

Authors
  1. C. Emmy Prema
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  3. S. Suresh
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Correspondence to C. Emmy Prema.

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Emmy Prema, C., Vinsley, S.S. & Suresh, S. Efficient Flame Detection Based on Static and Dynamic Texture Analysis in Forest Fire Detection. Fire Technol 54, 255–288 (2018). https://doi.org/10.1007/s10694-017-0683-x

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