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Early Smoke Detection in Outdoor Space: State-of-the-Art, Challenges and Methods

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Advances in Selected Artificial Intelligence Areas

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 24))

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Abstract

In recent decades, early smoke detection in outdoor environment is a hot topic due to its practical importance for a fire safety. Many researchers have contributed to this area since the 1990s. The chapter aims to follow the evolution of conventional image processing and machine learning methods based on the motion, semi-transparent, color, shape, texture and fractal features to deep learning solutions using various deep network architectures. Our experimental researches in this area have been conducted since 2010. This chapter reflects the original techniques of early smoke detection in complex outdoor scenes.

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Authors and Affiliations

  1. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy ave, Krasnoyarsk, 660037, Russian Federation

    Margarita N. Favorskaya

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  1. Margarita N. Favorskaya
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Correspondence to Margarita N. Favorskaya .

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Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  3. KES International, Shoreham-By-Sea, UK

    Lakhmi C. Jain

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Favorskaya, M.N. (2022). Early Smoke Detection in Outdoor Space: State-of-the-Art, Challenges and Methods. In: Virvou, M., Tsihrintzis, G.A., Jain, L.C. (eds) Advances in Selected Artificial Intelligence Areas. Learning and Analytics in Intelligent Systems, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-93052-3_8

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