Fabric Defect Classification Using Combination of Deep Learning and Machine Learning

Year 2021, Volume: 1 Issue: 1, 22 - 27, 30.08.2021

Fatma Günseli Yaşar Çıklaçandır Semih Utku Hakan Özdemir

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.

Keywords

Convolutional neural network, fabric defect classification, machine learning

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