A Novel Industrial Application of CNN Approach: Real Time Fabric Inspection and Defect Classification on Circular Knitting Machine

Year 2022, Volume: 32 Issue: 4, 344 - 352, 31.12.2022

Halil İbrahim Çelik Lale Canan Dülger Burak Öztaş Mehmet Kertmen Elif Gültekin

https://doi.org/10.32710/tekstilvekonfeksiyon.1017016

Cited By: 1

Abstract

Fabric Automatic Visual Inspection (FAVI) system provides reliable performance on fabric defects inspection. This study presents a machine vision system developed to adapt in circular knitting machines where fabric defects can be automatically controlled and detected defects can be classified. The knitted fabric surface are detected during real-time manufacturing. For the classification process, three different transfer learning architectures (ResNet-50, AlexNet, GoogLeNet) have been applied. The five common knitted fabric defects were recognized with the artificial intelligence-based software and classified with an average success rate of 98% using ResNet-50 architecture. The success rates of the trained networks were compared.

Keywords

Circular knitting machine, Knitted fabric, Defect detection, Deep learning, Convolutional neural network (CNNs)

Supporting Institution

Scientific and Technological Research Council of Turkey (TUBİTAK)

Project Number

5180057

Thanks

This study is supported by the Scientific and Technological Research Council of Turkey (TUBİTAK). Project Number: 5180057. We express our sincere thanks for their financial support.

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