Approach to prototyping a technical device for fire detection based on the principles of artificial intelligence

The aim of the study is to justify the developed prototype of a technical device that uses machine learning methods for early detection of fires in order to minimise damage and save human lives. The authors formulated a hypothesis about the expediency of expanding the frequency range within which the fire automation system detects the primary signs of fire in order to increase the efficiency of its operation. To test the hypothesis, a prototype has been developed, based on the typical structure of the existing fleet of fire automation systems, including an additional unit operating on the principles of artificial intelligence. The article describes an additional unit working on the principles of fuzzy logic and convolutional neural networks to analyse the data coming from video cameras and fire detectors in order to determine the fire in the early stages of ignition.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-05-16
DOI
https://doi.org/10.1051/e3sconf/202562702016/pdf
Primary Topic
Fire Detection and Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Approach to prototyping a technical device for fire detection based on the principles of artificial intelligence

A. V. Andreev, A. S. Doronin, B. Kh. Norov, Z. K. Mirkhasilova et al.
Springer Link (Chiba Institute of Technology)
Fire Detection and Safety Systems
article

Approach to prototyping a technical device for fire detection based on the principles of artificial intelligence

A. V. Andreev, A. S. Doronin, B. Kh. Norov, Z. K. Mirkhasilova, A. S. Abduganiev
article en

Abstract

The aim of the study is to justify the developed prototype of a technical device that uses machine learning methods for early detection of fires in order to minimise damage and save human lives. The authors formulated a hypothesis about the expediency of expanding the frequency range within which the fire automation system detects the primary signs of fire in order to increase the efficiency of its operation. To test the hypothesis, a prototype has been developed, based on the typical structure of the existing fleet of fire automation systems, including an additional unit operating on the principles of artificial intelligence. The article describes an additional unit working on the principles of fuzzy logic and convolutional neural networks to analyse the data coming from video cameras and fire detectors in order to determine the fire in the early stages of ignition.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 30%
Fire Detection and Safety Systems
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