Fabric Defect Classification Using Combination of Deep Learning and Machine Learning

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.

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06.06.2022 tarihinden bu yana
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13 Haziran 2024 11:11
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Detaylı Görünüm
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
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