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Methods of Solution to the Task on Early Detection of Fire Outbreaks Based on Images and Video Streams from Controlled Territories

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Data Science and Algorithms in Systems (CoMeSySo 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 597))

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Abstract

This paper deals with methods and approaches related to the solution of the task on early detection of fire outbreaks based on images and video streams from controlled territories. The images received from cameras, and also from unmanned aerial vehicles (UAVs), are exposed to automatic analysis, after which the conclusion about presence/absence of the fire within the controlled territory is made. In analysed studies of the last few years the artificial neural networks (ANN) are predominantly used for solving this task. ANN demonstrates high accuracy and recognition rate. This paper suggests the original method of detecting the fires in images and video streams, received from fixed cameras and UAV on-board cameras #COMESYSO11202.

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Authors and Affiliations

  1. Ailamazyan Program Systems Institute of Russian Academy of Sciences (PSI RAS), 4a, Peter I Street, Veskovo, Pereslavsky District, 152021, Russia

    Nikolay Abramov, Alexander Talalaev & Vitaly Fralenko

Authors
  1. Nikolay Abramov
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  2. Alexander Talalaev
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  3. Vitaly Fralenko
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Corresponding author

Correspondence to Nikolay Abramov .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Abramov, N., Talalaev, A., Fralenko, V. (2023). Methods of Solution to the Task on Early Detection of Fire Outbreaks Based on Images and Video Streams from Controlled Territories. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_6

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