Detection of surface anomalites on electric motors based on visual deep learning methods
| dc.authorscopusid | 58070535000 | |
| dc.authorscopusid | 55780618800 | |
| dc.contributor.author | Gozukirmizi,A.S. | |
| dc.contributor.author | Kivanc,O.C. | |
| dc.date.accessioned | 2024-05-25T12:34:30Z | |
| dc.date.available | 2024-05-25T12:34:30Z | |
| dc.date.issued | 2022 | |
| dc.department | Okan University | en_US |
| dc.department-temp | Gozukirmizi A.S., Istanbul Okan University, Department of Electric and Electronic Engineering, Istanbul, Turkey; Kivanc O.C., Istanbul Okan University, Department of Electric and Electronic Engineering, Istanbul, Turkey | en_US |
| dc.description | Batman University and Batman Energy Coordination Center (EKOM) | en_US |
| dc.description.abstract | Automotive industry is one of the most advanced industry among others due to the fact that engineering challenges, number of processes and other difficulties. Every component, electrical and mechanical parts produced must pass quality and performance tests in order to be assembled. For this inspection and test purpose, global brands (tier-one) and its related companies(tier-two) invest lots of industrial automation equipment to standardize production quality and minimize risks which can damage brand value, economical states and human safety. In addition to that digitalization and growing number of automation systems are core features of Industry 4.0 concept which is a global trend and automotive is leading industry. All of mentioned inspection tasks are essential for all automotive industry including sub-industries, especially in electric cars are increasing trend and will become dominant against fuel vehicles in next 10 years. © 2022 IEEE. | en_US |
| dc.identifier.citationcount | 1 | |
| dc.identifier.doi | 10.1109/GEC55014.2022.9986676 | |
| dc.identifier.endpage | 216 | en_US |
| dc.identifier.isbn | 978-166549751-0 | |
| dc.identifier.scopus | 2-s2.0-85146488193 | |
| dc.identifier.startpage | 208 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/GEC55014.2022.9986676 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14517/2589 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | IEEE Global Energy Conference, GEC 2022 -- 2022 IEEE Global Energy Conference, GEC 2022 -- 26 October 2022 through 29 October 2022 -- Batman -- 185674 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.scopus.citedbyCount | 1 | |
| dc.subject | deep learning | en_US |
| dc.subject | electric car component inspection | en_US |
| dc.subject | machine vision | en_US |
| dc.subject | metallic surface inspection | en_US |
| dc.subject | robot vision | en_US |
| dc.subject | transfer learning | en_US |
| dc.title | Detection of surface anomalites on electric motors based on visual deep learning methods | en_US |
| dc.type | Conference Object | en_US |