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COMPUTER VISION METHOD FOR FOREST FIRES DETECTION BASED ON RGB IMAGES OBTAINED BY UNMANNED MOTOR GLIDER

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dc.contributor.authorKataev, M. Yu.
dc.contributor.authorKartashov, E. Yu.
dc.date.accessioned2024-11-30T04:09:12Z
dc.date.available2024-11-30T04:09:12Z
dc.date.issued2021
dc.description.abstractThe article proposes a method (algorithm) of forest fire detection by means of RGB images obtained by using an unmanned aerial vehicle (motor glid-er). It includes several stages associated with back-ground detection and subtraction and recognition of fire areas by means of RGB colour space. The proposed method was tested using images of forest fires. It is proposed to use unmanned aerial vehicles capable to monitor large areas continuous-ly for several hours. The results of calculations are shown, which demonstrate that the proposed method allows us to detect areas of images occupied by forest fires and may be used in automatic forest fire monitoring systems. © 2021, LLC Editorial of Journal ""Light Technik"". All rights reserved.
dc.format.extentС. 71-78
dc.identifier.citationKataev, M. Yu. COMPUTER VISION METHOD FOR FOREST FIRES DETECTION BASED ON RGB IMAGES OBTAINED BY UNMANNED MOTOR GLIDER / Kataev, M.Yu., Kartashov, E.Yu. // Light and Engineering. - 2021. - 29. - № 5. - P. 71-78. - 10.33383/2021-009
dc.identifier.doi10.33383/2021-009
dc.identifier.urihttps://www.doi.org/10.33383/2021-009
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85135219223&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/25430
dc.relation.ispartofLight and Engineering
dc.subjectDetection criterion
dc.subjectForest fire
dc.subjectImage
dc.subjectRGB image
dc.subjectUnmanned aerial vehicle (UAV)
dc.titleCOMPUTER VISION METHOD FOR FOREST FIRES DETECTION BASED ON RGB IMAGES OBTAINED BY UNMANNED MOTOR GLIDER
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.volume29
relation.isOrgUnitOfPublicationf2e6b525-f6bc-4c2e-a30f-2e3159d9a463
relation.isOrgUnitOfPublication.latestForDiscoveryf2e6b525-f6bc-4c2e-a30f-2e3159d9a463
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