dc.contributor.author | Guney E. | |
dc.contributor.author | Sahin I.H. | |
dc.contributor.author | Cakar S. | |
dc.contributor.author | Atmaca O. | |
dc.contributor.author | Erol E. | |
dc.contributor.author | Doganli M. | |
dc.contributor.author | Bayilmis C. | |
dc.date.accessioned | 2023-03-14T20:29:07Z | |
dc.date.available | 2023-03-14T20:29:07Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 9.78167E+12 | |
dc.identifier.uri | https://doi.org/10.1109/ISMSIT56059.2022.9932841 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14002/1614 | |
dc.description | 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- -- 18435 | en_US |
dc.description.abstract | The 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.sponsorship | 3191247 | en_US |
dc.description.sponsorship | This work is supported by the TUBITAK TEYDEB (project no.: 3191247) "Robotic Electric Ship Battery Supply System (REGBES)". | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | automated charging | en_US |
dc.subject | computer vision | en_US |
dc.subject | electric vehicles | en_US |
dc.subject | object detection | en_US |
dc.subject | Shore-To-ship charging | en_US |
dc.subject | Cameras | en_US |
dc.subject | Charging (batteries) | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Electric vehicles | en_US |
dc.subject | Learning systems | en_US |
dc.subject | Object detection | en_US |
dc.subject | Real time systems | en_US |
dc.subject | Automated charging | en_US |
dc.subject | Automatic charging | en_US |
dc.subject | Carbon emissions | en_US |
dc.subject | Charging systems | en_US |
dc.subject | Detection system | en_US |
dc.subject | Electric and hybrid vehicles | en_US |
dc.subject | Images processing | en_US |
dc.subject | Objects detection | en_US |
dc.subject | Real- time | en_US |
dc.subject | Shore-to-ship charging | en_US |
dc.subject | Ships | en_US |
dc.title | Electric Shore-To-Ship Charging Socket Detection Using Image Processing and YOLO | en_US |
dc.type | conferenceObject | en_US |
dc.department | Belirlenecek | en_US |
dc.identifier.doi | 10.1109/ISMSIT56059.2022.9932841 | |
dc.identifier.startpage | 1069 | en_US |
dc.identifier.endpage | 1073 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57696129400 | |
dc.authorscopusid | 57985157700 | |
dc.authorscopusid | 57984626600 | |
dc.authorscopusid | 57210415748 | |
dc.authorscopusid | 56337080200 | |
dc.authorscopusid | 57226704301 | |
dc.authorscopusid | 8645866100 | |
dc.identifier.scopus | 2-s2.0-85142840612 | en_US |