Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning

dc.contributor.author Ozel, Berk
dc.contributor.author Alam, Muhammad Shahab
dc.contributor.author Khan, Muhammad Umer
dc.date.accessioned 2024-11-05T20:18:59Z
dc.date.available 2024-11-05T20:18:59Z
dc.date.issued 2024
dc.description.abstract Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning. en_US
dc.description.sponsorship This research received no external funding. en_US
dc.identifier.doi 10.3390/info15090538
dc.identifier.issn 2078-2489
dc.identifier.scopus 2-s2.0-85205255797
dc.identifier.uri https://doi.org/10.3390/info15090538
dc.identifier.uri https://hdl.handle.net/20.500.14411/10253
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.ispartof Information
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject artificial intelligence en_US
dc.subject deep learning en_US
dc.subject detection en_US
dc.subject fire en_US
dc.subject flame en_US
dc.subject forest fire en_US
dc.subject smoke en_US
dc.subject wildfire en_US
dc.title Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning en_US
dc.type Review en_US
dspace.entity.type Publication
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gdc.bip.impulseclass C3
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::review
gdc.collaboration.industrial false
gdc.description.department Atılım University en_US
gdc.description.departmenttemp [Ozel, Berk; Khan, Muhammad Umer] Atilim Univ, Dept Mechatron Engn, TR-06830 Ankara, Turkiye; [Alam, Muhammad Shahab] Gebze Tech Univ, Def Technol Inst, TR-41400 Gebze, Turkiye en_US
gdc.description.issue 9 en_US
gdc.description.publicationcategory Diğer en_US
gdc.description.scopusquality Q2
gdc.description.startpage 538
gdc.description.volume 15 en_US
gdc.description.woscitationindex Emerging Sources Citation Index
gdc.description.wosquality Q2
gdc.identifier.openalex W4402191377
gdc.identifier.wos WOS:001323578100001
gdc.index.type WoS
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gdc.oaire.diamondjournal false
gdc.oaire.impulse 32.0
gdc.oaire.influence 4.3617683E-9
gdc.oaire.isgreen false
gdc.oaire.keywords flame
gdc.oaire.keywords detection
gdc.oaire.keywords deep learning
gdc.oaire.keywords Information technology
gdc.oaire.keywords artificial intelligence
gdc.oaire.keywords T58.5-58.64
gdc.oaire.keywords fire
gdc.oaire.keywords forest fire
gdc.oaire.popularity 2.606623E-8
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gdc.openalex.collaboration National
gdc.openalex.fwci 21.43420878
gdc.openalex.normalizedpercentile 0.99
gdc.openalex.toppercent TOP 1%
gdc.opencitations.count 0
gdc.plumx.mendeley 56
gdc.plumx.newscount 1
gdc.plumx.scopuscites 28
gdc.scopus.citedcount 28
gdc.virtual.author Khan, Muhammad Umer
gdc.wos.citedcount 17
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