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Machine Learning and Color Treatment for the Forest Fire and Smoke Detection Systems and Algorithms, a Recent Literature Review

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Artificial Intelligence, Computer and Software Engineering Advances (CIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1326))

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Abstract

Currently, the forest fires are worldwide problem which requires a complete solution. The consequences of it are related, not only, to the environmental and biodiversity destruction but also to human and economic losses. In this article, forest fire and smoke detection systems, methods and technics literature review are carried out. To accomplish this aim, a generic mobile and non-mobile monitoring and surveillance systems applied in this field analysis took place. From the review carried out, it has been found that for fire detection two types of methods have been used, one based on machine learning and another through color features extraction of images or video frames, on the other hand, the combination of both methods is used for the detection of smoke and fire or only smoke, this guarantees accurate rates of more than 90%. Finally, a detailed analysis of the use of these fire and smoke detection algorithms in the UAS/UAV’s systems was carried out paying special attention on them due to the flexibility, versatility, and maneuverability abilities among others displayed by these systems that help prevent and mitigate forest fires.

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Acknowledgements

This work was partially supported by the PIS-04 “Detección temprana de incendios forestales basado en aprendizaje automático y en algoritmos de clasificación aplicados al espectro visible” research project of the Universidad de las Fuerzas Armadas ESPE, Ecuador and partially supported by the Work Group “Internet de las Cosas y Ciudades Inteligentes” of the Corporación Ecuatoriana para el Desarrollo de la Investigación y la Academia (CEDIA), Ecuador.

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Authors and Affiliations

  1. Universidad de Las Fuerzas Armadas - ESPE, Sangolquí, Ecuador

    Henry Cruz, Tatiana Gualotuña, Diego Marcillo, Santiago Jácome & Efraín R. Fonseca C.

  2. Escuela Politécnica de Chimborazo, Riobamba, Ecuador

    María Pinillos

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  1. Henry Cruz
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  2. Tatiana Gualotuña
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  3. María Pinillos
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  4. Diego Marcillo
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  5. Santiago Jácome
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  6. Efraín R. Fonseca C.
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Correspondence to Henry Cruz .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Henry Cruz

  3. University of Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

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Cruz, H., Gualotuña, T., Pinillos, M., Marcillo, D., Jácome, S., Fonseca C., E.R. (2021). Machine Learning and Color Treatment for the Forest Fire and Smoke Detection Systems and Algorithms, a Recent Literature Review. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A. (eds) Artificial Intelligence, Computer and Software Engineering Advances. CIT 2020. Advances in Intelligent Systems and Computing, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-68080-0_8

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