Näytä suppeat kuvailutiedot

dc.contributor.authorRaita-Hakola, A.-M.
dc.contributor.authorRahkonen, S.
dc.contributor.authorSuomalainen, J.
dc.contributor.authorMarkelin, L.
dc.contributor.authorOliveira, R.
dc.contributor.authorHakala, T.
dc.contributor.authorKoivumäki, N.
dc.contributor.authorHonkavaara, E.
dc.contributor.authorPölönen, I.
dc.contributor.editorEl-Sheimy, N.
dc.contributor.editorAbdelbary, A.A
dc.contributor.editorEl-Bendary, N.
dc.contributor.editorMohasseb,Y
dc.date.accessioned2024-06-05T12:32:51Z
dc.date.available2024-06-05T12:32:51Z
dc.date.issued2023
dc.identifier.citationRaita-Hakola, A.-M., Rahkonen, S., Suomalainen, J., Markelin, L., Oliveira, R., Hakala, T., Koivumäki, N., Honkavaara, E., & Pölönen, I. (2023). Combining YOLO V5 and transfer learning for smoke-based wildfire detection on boreal forests. In N. El-Sheimy, A. Abdelbary, N. El-Bendary, & Y. Mohasseb (Eds.), <i>ISPRS Geospatial Week 2023</i> (pp. 1771-1778). Copernicus publications. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023. <a href="https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023" target="_blank">https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023</a>
dc.identifier.otherCONVID_216042115
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/95557
dc.description.abstractWildfires present severe threats to various aspects of ecosystems, human settlements, and the environment. Early detection plays a critical role in minimizing the destructive consequences of wildfires. This study introduces an innovative approach for smoke-based wildfire detection in Boreal forests by combining the YOLO V5 algorithm and transfer learning. YOLO V5 is renowned for its real-time performance and accuracy in object detection. Given the scarcity of labelled smoke images specific to wildfire scenes, transfer learning techniques are employed to address this limitation. Initially, the generalisability of smoke as an object is examined by utilising wildfire data collected from diverse environments for fine-tuning and testing purposes in Boreal forest scenarios. Subsequently, Boreal forest fire data is employed for training and fine-tuning to achieve high detection accuracy and explore benchmarks for effective local training data. This approach minimises extensive manual labelling efforts while enhancing the accuracy of smoke-based wildfire detection in Boreal forest environments. Experimental results validate the efficacy of the proposed approach. The combined YOLO V5 and transfer learning framework demonstrates a high detection accuracy, making it a promising solution for automated wildfire detection systems. Implementing this methodology can potentially enhance early detection and response to wildfires in Boreal forest regions, thereby contributing to improved disaster management and mitigationen
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherCopernicus publications
dc.relation.ispartofISPRS Geospatial Week 2023
dc.relation.ispartofseriesInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.rightsCC BY 4.0
dc.subject.otherboreal forests
dc.subject.otherforest fire
dc.subject.othersmoke detection
dc.subject.othertransfer learning
dc.subject.otherwildfire detection
dc.subject.otherYOLO V5
dc.titleCombining YOLO V5 and transfer learning for smoke-based wildfire detection on boreal forests
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202406054315
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1771-1778
dc.relation.issn1682-1750
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Society for Photogrammetry and Remote Sensing Congress
dc.relation.grantnumber348009
dc.subject.ysomaastopalot
dc.subject.ysokoneoppiminen
dc.subject.ysometsäpalot
dc.subject.ysokaukovalvonta
dc.subject.ysosavu
dc.subject.ysoilmakuvat
dc.subject.ysokonenäkö
dc.subject.ysoboreaalinen vyöhyke
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8580
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p11855
jyx.subject.urihttp://www.yso.fi/onto/yso/p6926
jyx.subject.urihttp://www.yso.fi/onto/yso/p15959
jyx.subject.urihttp://www.yso.fi/onto/yso/p2524
jyx.subject.urihttp://www.yso.fi/onto/yso/p2618
jyx.subject.urihttp://www.yso.fi/onto/yso/p16692
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-archives-XLVIII-1-W2-2023-1771-2023
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramOthers, AoFen
jyx.fundingprogramMuut, SAfi
jyx.fundinginformationThis study is funded by the Academy of Finland (Grant No. 348009 and 346710).
dc.type.okmA4


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