Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30822
DC FieldValueLanguage
dc.contributor.authorMelillos, George-
dc.contributor.authorKalogirou, Eleftheria-
dc.contributor.authorMakri, Despina-
dc.contributor.authorHadjimitsis, Diofantos G.-
dc.date.accessioned2023-11-20T11:09:32Z-
dc.date.available2023-11-20T11:09:32Z-
dc.date.issued2023-09-21-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30822-
dc.description.abstractThis paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using YOLO deep learning framework. YOLO (You Only Look Once) is an object detection algorithm Object detection algorithms using region proposal includes RCNN, Fast RCNN, and Faster RCNN, etc. Region based Convolutional Neural Networks (RCNN) algorithm uses a group of boxes for the image and then analyses in each box if either of the boxes holds a target. It employs the method of selective search to pick those sections from the picture. YOLO can be used to assist in making safety checks for ships and mariners. 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. The preliminary 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.description.sponsorshipERATOSTHENES Centre of Excellenceen_US
dc.formatPDFen_US
dc.language.isoenen_US
dc.relationEXCELSIOR: ERATOSTHENES Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment : Teaming Phase1 GA 763643en_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectYOLOen_US
dc.subjectRemote Sensingen_US
dc.subjectSentinel-1en_US
dc.subjectShip detectionen_US
dc.titleShip detection using SAR images based on YOLO (you only look once)en_US
dc.typeConference Papersen_US
dc.collaborationERATOSTHENES Centre of Excellenceen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.subject.categoryENGINEERING AND TECHNOLOGYen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationNon Peer Revieweden_US
dc.relation.conferenceNinth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprusen_US
dc.identifier.doihttps://doi.org/10.1117/12.2681665en_US
cut.common.academicyear2022-2023en_US
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.languageiso639-1en-
crisitem.project.funderEC-
crisitem.project.grantnoH2020-WIDESPREAD-04-2017-
crisitem.project.fundingProgramH2020-
crisitem.project.openAireinfo:eu-repo/grantAgreement/EC/H2020/763643-
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.orcid0009-0005-0188-0200-
crisitem.author.orcid0009-0002-6217-9328-
crisitem.author.orcid0000-0002-2684-547X-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:EXCELSIOR H2020 Teaming Project Publications
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