Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2381
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dc.contributor.authorÖrnek, Ahmet Haydar-
dc.contributor.authorÇelik, Mustafa-
dc.contributor.authorCeylan, Murat-
dc.date.accessioned2022-05-23T20:07:31Z-
dc.date.available2022-05-23T20:07:31Z-
dc.date.issued2021-
dc.identifier.isbn9781665410700-
dc.identifier.urihttps://doi.org/10.1109/UYMS54260.2021.9659582-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2381-
dc.description15th Turkish National Software Engineering Symposium, UYMS 2021 -- 17 November 2021 through 19 November 2021 -- -- 176220en_US
dc.description.abstractIt is vital important for people to wear masks during the pandemic that affects the whole world. In this study, it was detected whether people wear masks by using convolutional neural networks which is one of the deep learning methods and transfer learning. In the classification carried out using the Resnet-18 architecture, both real-life images obtained with the Huawei M2150 camera and images shared as open source were used. The system, which was trained using 18600 images, was tested with 4540 real-world images and 95.16% sensitivity 96.69% specificity values were obtained. Thus, a model that works with high performance not only on high resolution images taken close up, but also on low resolution images taken from afar was obtained. © 2021 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 Turkish National Software Engineering Symposium, UYMS 2021 - Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectmask detectionen_US
dc.subjecttransfer learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectFace recognitionen_US
dc.subjectTransfer learningen_US
dc.subjectWear of materialsen_US
dc.subjectConvolutional neural networken_US
dc.subjectDeep learningen_US
dc.subjectFace imagesen_US
dc.subjectLearning methodsen_US
dc.subjectLearning Transferen_US
dc.subjectMask detectionen_US
dc.subjectOpen-sourceen_US
dc.subjectReal-life imagesen_US
dc.subjectReal-world imageen_US
dc.subjectTransfer learningen_US
dc.subjectDeep learningen_US
dc.titleMask Detection from Face Images Using Deep Learning and Transfer Learningen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/UYMS54260.2021.9659582-
dc.identifier.scopus2-s2.0-85124797403en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.wosWOS:000813101100022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57210593918-
dc.authorscopusid57457105000-
dc.authorscopusid56276648900-
item.grantfulltextembargo_20300101-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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