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Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People
Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572756/ https://www.ncbi.nlm.nih.gov/pubmed/36236403 http://dx.doi.org/10.3390/s22197305 |
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author | Abdusalomov, Akmalbek Bobomirzaevich Mukhiddinov, Mukhriddin Kutlimuratov, Alpamis Whangbo, Taeg Keun |
author_facet | Abdusalomov, Akmalbek Bobomirzaevich Mukhiddinov, Mukhriddin Kutlimuratov, Alpamis Whangbo, Taeg Keun |
author_sort | Abdusalomov, Akmalbek Bobomirzaevich |
collection | PubMed |
description | Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices. |
format | Online Article Text |
id | pubmed-9572756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95727562022-10-17 Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People Abdusalomov, Akmalbek Bobomirzaevich Mukhiddinov, Mukhriddin Kutlimuratov, Alpamis Whangbo, Taeg Keun Sensors (Basel) Article Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices. MDPI 2022-09-26 /pmc/articles/PMC9572756/ /pubmed/36236403 http://dx.doi.org/10.3390/s22197305 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Abdusalomov, Akmalbek Bobomirzaevich Mukhiddinov, Mukhriddin Kutlimuratov, Alpamis Whangbo, Taeg Keun Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title | Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title_full | Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title_fullStr | Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title_full_unstemmed | Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title_short | Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People |
title_sort | improved real-time fire warning system based on advanced technologies for visually impaired people |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572756/ https://www.ncbi.nlm.nih.gov/pubmed/36236403 http://dx.doi.org/10.3390/s22197305 |
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