Signal, Image and Video Processing

, Volume 10, Issue 4, pp 647–654 | Cite as

Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods

  • Tom ToulouseEmail author
  • Lucile Rossi
  • Turgay Celik
  • Moulay Akhloufi
Original Paper


This paper presents a comparative analysis of state-of-the art image processing-based fire color detection rules and methods in the context of geometrical characteristics measurement of wildland fires. Two new rules and two new detection methods using an intelligent combination of the rules are presented, and their performances are compared with their counterparts. The benchmark is performed on approximately two hundred million fire pixels and seven hundred million non-fire pixels extracted from five hundred wildland images under diverse imaging conditions. The fire pixels are categorized according to fire color and existence of smoke; meanwhile, non-fire pixels are categorized according to the average intensity of the corresponding image. This characterization allows to analyze the performance of each rule by category. It is shown that the performances of the existing rules and methods from the literature are category dependent, and none of them is able to perform equally well on all categories. Meanwhile, a new proposed method based on machine learning techniques and using all the rules as features outperforms existing state-of-the-art techniques in the literature by performing almost equally well on different categories. Thus, this method, promises very interesting developments for the future of metrologic tools for fire detection in unstructured environments.


Fire pixel detection Rules Machine learning  Wildland fire 


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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Tom Toulouse
    • 1
    • 5
    Email author
  • Lucile Rossi
    • 1
  • Turgay Celik
    • 2
    • 3
  • Moulay Akhloufi
    • 4
  1. 1.UMR CNRS 6134 SPEUniversity of CorsicaCorteFrance
  2. 2.School of Computer ScienceUniversity of the WitwatersrandJohannesburgSouth Africa
  3. 3.Electrical and Electronics EngineeringMeliksah UniversityKayseriTurkey
  4. 4.Electronics EngineeringUniversidad Tecnica Federico Santa MaríaValparaisoChile
  5. 5.Electrical and Computer EngineeringLaval UniversityQuebecCanada

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