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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorMelillos, George-
dc.contributor.authorHadjimitsis, Diofantos G.-
dc.contributor.editorPalaniappan, Kannappan-
dc.contributor.editorSeetharaman, Gunasekaran-
dc.contributor.editorHarguess, Joshua D.-
dc.date.accessioned2023-09-15T09:21:41Z-
dc.date.available2023-09-15T09:21:41Z-
dc.date.issued2022-06-06-
dc.identifier.citationGeospatial Informatics XII 2022Virtual, Online, 6 - 12 June 2022en_US
dc.identifier.isbn9781510650749-
dc.identifier.issn0277786X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30408-
dc.description.abstractThis paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using YOLO deep learning framework. The You Only Look Once (YOLO) model was initially introduced as the first object detection model that combined bounding box prediction and objects classification into a single end-to-end differentiable network. We train the YOLO model on our dataset in this paper for our detector to learn to detect objects in SAR images such as ships. YOLO test results showed an increase in the accuracy of ship detection at Cyprus's Coast and can be applied in the field of ship detection.en_US
dc.language.isoenen_US
dc.rights© SPIEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCyprusen_US
dc.subjectShip detectionen_US
dc.subjectsynthetic aperture radar (SAR) imagesen_US
dc.subjectYou Only Look Once (YOLO)en_US
dc.titleShip detection using sar images based on yolo at Cyprus’s coasten_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationERATOSTHENES Centre of Excellenceen_US
dc.subject.categoryCivil Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.identifier.doi10.1117/12.2614526en_US
dc.identifier.scopus2-s2.0-85136136672-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85136136672-
dc.relation.volume12099en_US
cut.common.academicyear2021-2022en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-8292-1836-
crisitem.author.orcid0000-0002-2684-547X-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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