Automatic systems can be used in many areas, such as the production stage in factories, country defense, and traffic control. They provide the opportunity to reach results faster with higher success rates thanks to human-computer vision cooperation. In this study, it is aimed to develop an intelligent system that automatically detects and classifies defects in fabrics. Thanks to the developed system, the cause of the malfunction is eliminated, and the recurrence of the malfunction is prevented. Using deep learning methods in fabric defect classification studies has a disadvantage compared to other methods. Multiple layers in deep learning cause a time-consuming process. Therefore, a combination of Deep Learning and Support Vector Machines (SVM) has been used in this study. The success of the provided system has been compared with other deep learning algorithms in terms of time and accuracy.
Eser Adı (dc.title) | Fabric Defect Classification Using Combination of Deep Learning and Machine Learning |
Eser Sahibi (dc.contributor.author) | Fatma Günseli YAŞAR ÇIKLAÇANDIR |
Yayın Tarihi (dc.date.issued) | 2021 |
Diğer Yazarlar (dc.contributor.authors) | Hakan ÖZDEMİR |
Diğer Yazarlar (dc.contributor.authors) | Semih UTKU |
Yayıncı (dc.publisher) | İzmir Katip Çelebi Üniversitesi |
Tür (dc.type) | Makale |
Özet (dc.description.abstract) | Automatic systems can be used in many areas, such as the production stage in factories, country defense, and traffic control. They provide the opportunity to reach results faster with higher success rates thanks to human-computer vision cooperation. In this study, it is aimed to develop an intelligent system that automatically detects and classifies defects in fabrics. Thanks to the developed system, the cause of the malfunction is eliminated, and the recurrence of the malfunction is prevented. Using deep learning methods in fabric defect classification studies has a disadvantage compared to other methods. Multiple layers in deep learning cause a time-consuming process. Therefore, a combination of Deep Learning and Support Vector Machines (SVM) has been used in this study. The success of the provided system has been compared with other deep learning algorithms in terms of time and accuracy. |
Kayıt Giriş Tarihi (dc.date.accessioned) | 06.06.2022 |
Açık Erişim Tarihi (dc.date.available) | 2022-06-06 |
Yayın Dili (dc.language.iso) | eng |
Konu Başlıkları (dc.subject) | Convolutional neural network |
Konu Başlıkları (dc.subject) | Fabric defect classification |
Konu Başlıkları (dc.subject) | Machine learning |
Atıf için Künye (dc.identifier.citation) | F. G. Yaşar Çıklaçandır , S. Utku ve H. Özdemir , "Fabric Defect Classification Using Combination of Deep Learning and Machine Learning", Journal of Artificial Intelligence and Data Science, c. 1, sayı. 1, ss. 22-27, Ağu. 2021 |
ISSN (dc.identifier.issn) | 2791-8335 |
Yayının ilk sayfa sayısı (dc.identifier.startpage) | 22 |
Yayının son sayfa sayısı (dc.identifier.endpage) | 27 |
Dergi Adı (dc.relation.journal) | Journal of Artificial Intelligence and Data Science |
Dergi Sayısı (dc.identifier.issue) | 1 |
Dergi Cilt (dc.identifier.volume) | 1 |
Haklar (dc.rights) | Open access |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/11469/1924 |