Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1916
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dc.contributor.authorTunal Mustafa Mert-
dc.contributor.authorYildiz Ahmet-
dc.contributor.authorÇakar Tuna-
dc.date.accessioned2023-03-06T06:53:18Z
dc.date.available2023-03-06T06:53:18Z
dc.date.issued2022-
dc.identifier.citationTunali, M. M., Yildiz, A., & Cakar, T. (2022). Steel Surface Defect Classification Via Deep Learning. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.9919470en_US
dc.identifier.isbn9781670000000-
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1916-
dc.identifier.urihttps://doi.org/10.1109/UBMK55850.2022.9919470-
dc.description.abstractDeep learning and image processing methods have taken place in many parts of our lives, as well as in the quality control stages of production lines. The aim of this study is to train and use a deep learning model to improve quality management using limited data and computing power. To achieve that, deep learning for quality control models were trained by classifying six different steel surface defect images in the NEU-DET dataset. Xception, ResNetV2 152, VGG19 and InceptionV3 architectures were used to train the model. High accuracy was obtained with both Xception and ResNetV2 152. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectImage Classificationen_US
dc.subjectInception V3en_US
dc.subjectNEU-DETen_US
dc.subjectQuality Controlen_US
dc.subjectResNet152 V2en_US
dc.subjectSteel Surface Defecten_US
dc.subjectVGG19en_US
dc.subjectXceptionen_US
dc.titleSteel Surface Defect Classification Via Deep Learningen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/UBMK55850.2022.9919470-
dc.identifier.scopus2-s2.0-85141881108en_US
dc.authoridÇakar, Tuna / 0000-0001-8594-7399-
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanıen_US
dc.identifier.startpage485 - 489en_US
dc.departmentMühendislik Fakültesi, Bilgisayar Mühendisligi Bölümüen_US
dc.relation.journalProceedings - 7th International Conference on Computer Science and Engineering, Ubmk 2022en_US
dc.institutionauthorTunal, Mustafa Mert, Yildiz, Ahmet, Çakar, Tuna-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeConference Object-
crisitem.author.dept02.02. Department of Computer Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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