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dc.contributor.authorGuney E.
dc.contributor.authorSahin I.H.
dc.contributor.authorCakar S.
dc.contributor.authorAtmaca O.
dc.contributor.authorErol E.
dc.contributor.authorDoganli M.
dc.contributor.authorBayilmis C.
dc.date.accessioned2023-03-14T20:29:07Z
dc.date.available2023-03-14T20:29:07Z
dc.date.issued2022
dc.identifier.isbn9.78167E+12
dc.identifier.urihttps://doi.org/10.1109/ISMSIT56059.2022.9932841
dc.identifier.urihttps://hdl.handle.net/20.500.14002/1614
dc.description6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- -- 18435en_US
dc.description.abstractThe production of electric and hybrid vehicles on land and at sea is now widely used to reduce carbon emissions. While charging the batteries of electric vehicles can be done manually, studies to automate this process are increasing. Recently, many studies based on computer vision have been carried out to provide real-Time and more accurate detection of charging systems. For automatic charging, the position and distance of the socket on the ship approaching the shore can be determined by processing the image taken by the camera. For this purpose, in this study, an interface has been developed for the detection system of electrical charging system sockets by using classical image processing and the YOLO technique, which is one of the deep learning methods. With the developed interface, the socket's position can be detected and monitored in real-Time through the image taken from the camera. Thus, automatic charging can be performed successfully. © 2022 IEEE.en_US
dc.description.sponsorship3191247en_US
dc.description.sponsorshipThis work is supported by the TUBITAK TEYDEB (project no.: 3191247) "Robotic Electric Ship Battery Supply System (REGBES)".en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectautomated chargingen_US
dc.subjectcomputer visionen_US
dc.subjectelectric vehiclesen_US
dc.subjectobject detectionen_US
dc.subjectShore-To-ship chargingen_US
dc.subjectCamerasen_US
dc.subjectCharging (batteries)en_US
dc.subjectComputer visionen_US
dc.subjectDeep learningen_US
dc.subjectElectric vehiclesen_US
dc.subjectLearning systemsen_US
dc.subjectObject detectionen_US
dc.subjectReal time systemsen_US
dc.subjectAutomated chargingen_US
dc.subjectAutomatic chargingen_US
dc.subjectCarbon emissionsen_US
dc.subjectCharging systemsen_US
dc.subjectDetection systemen_US
dc.subjectElectric and hybrid vehiclesen_US
dc.subjectImages processingen_US
dc.subjectObjects detectionen_US
dc.subjectReal- timeen_US
dc.subjectShore-to-ship chargingen_US
dc.subjectShipsen_US
dc.titleElectric Shore-To-Ship Charging Socket Detection Using Image Processing and YOLOen_US
dc.typeconferenceObjecten_US
dc.departmentBelirleneceken_US
dc.identifier.doi10.1109/ISMSIT56059.2022.9932841
dc.identifier.startpage1069en_US
dc.identifier.endpage1073en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57696129400
dc.authorscopusid57985157700
dc.authorscopusid57984626600
dc.authorscopusid57210415748
dc.authorscopusid56337080200
dc.authorscopusid57226704301
dc.authorscopusid8645866100
dc.identifier.scopus2-s2.0-85142840612en_US


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