Detecting of Circular Knitting Fabric Defects Using VGG16 Architecture

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dc.contributor.authorHanbay, Kazım
dc.date.accessioned2024-08-04T19:54:40Z
dc.date.available2024-08-04T19:54:40Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAlthough the conventional image processing methods can detect fabric defects, fabric defect detection is an open research problem due to the diversity of defect types. In this paper, the feasibility of VGG16 deep learning architecture for fabric defect detection has been demonstrated. A new fabric defect database is used. The pre-trained model of VGG16 architecture on the new database is built. Thus, the training time of the model is reduced. The experimental results show that the VGG16 model outperforms the traditional Shearlet transform and GLCM methods.en_US
dc.identifier.doi10.46810/tdfd.1105343
dc.identifier.endpage129en_US
dc.identifier.issn2149-6366
dc.identifier.issue2en_US
dc.identifier.startpage125en_US
dc.identifier.trdizinid1100209en_US
dc.identifier.urihttps://doi.org/10.46810/tdfd.1105343
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1100209
dc.identifier.urihttps://hdl.handle.net/11616/90035
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTürk Doğa ve Fen Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleDetecting of Circular Knitting Fabric Defects Using VGG16 Architectureen_US
dc.typeArticleen_US

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