Fabric Defect Classification Using Combination of Deep Learning and Machine Learning

Yıl 2021, Cilt: 1 Sayı: 1, 22 - 27, 30.08.2021

Fatma Günseli Yaşar Çıklaçandır Semih Utku Hakan Özdemir

Öz

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.

Anahtar Kelimeler

Convolutional neural network, fabric defect classification, machine learning

Kaynakça

• [1] ISO (1990) "Woven Fabrics – Description of defects – Vocabulary," ISO 8498: 1990 (E/F).
• [2] B. Barış, and H. Z. Özek, “Dokuma Kumaş Hatalarının Sistematik Sınıflandırılması Üzerine Bir Çalışma,” Tekstil ve Mühendis, vol. 26, no. 114, pp.156-167, 2019.
• [3] Z. Zhu, G. Han, G. Jia, and L. Shu, “Modified densenet for automatic fabric defect detection with edge computing for minimizing latency,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9623-9636, 2020.
• [4] Y. Huang, J. Jing, and Z. Wang, “Fabric Defect Segmentation Method Based on Deep Learning,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-15, 2021.
• [5] V. V. Karlekar, M. S. Biradar, and K. B. Bhangale, “Fabric defect detection using wavelet filter,” In 2015 International Conference on Computing Communication Control and Automation, pp. 712-715, 2015.
• [6] X. Chang, C. Gu, J. Liang, and X. Xu, “Fabric defect detection based on pattern template correction,” Mathematical Problems in Engineering, 2018.
• [7] B. Wei, K. Hao, X. S. Tang, and Y. Ding, “A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes,” Textile Research Journal, vol. 89, no. 17, pp. 3539-3555, 2019.
• [8] K. Basibuyuk, K. Coban, and A. Ertuzun, “Model based defect detection problem: Particle filter approach,” In 2008 3rd International Symposium on Communications, Control and Signal Processing, pp. 348-351, 2008.
• [9] O. G. Sezer, A. Erçil, and A. Ertuzun, “Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection,” Pattern Recognition, vol. 40, no. 1, pp. 121-133, 2007.
• [10] L. Bissi, G. Baruffa, P. Placidi, E. Ricci, A. Scorzoni, and P. Valigi, “Automated defect detection in uniform and structured fabrics using gabor filters and PCA,” Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 838-845, 2013.
• [11] J. Jing, P. Yang, P. Li, and X. Kang, “Supervised defect detection on textile fabrics via optimal gabor filter,” Journal of Industrial Textiles, vol. 44, no. 1, pp. 40-57, 2014.
• [12] N. Kure, M. S. Biradar, and K. B. Bhangale, ”Local neighborhood analysis for fabric defect detection,” In 2017 International Conference on Information, Communication, Instrumentation and Control, pp. 1-5, 2017.
• [13] J. Cao, J. Zhang, Z. Wen, N. Wang, and X. Liu, “Fabric defect inspection using prior knowledge guided least squares regression,” Multimedia Tools and Applications, vol. 76, no. 3, pp. 4141-4157, 2017.
• [14] L. Liu, J. Zhang, X. Fu, L. Liu, and Q. Huang, “Unsupervised segmentation and elm for fabric defect image classification,” Multimedia Tools and Applications, vol. 78, no. 9, pp. 12421-12449, 2019.
• [15] J.F. Jing, H. Ma, and H. H. Zhang, “Automatic fabric defect detection using a deep convolutional neural network,” Coloration Technology, vol. 135, no. 3, pp. 213-223, 2019.
• [16] P. R. Jeyaraj, and E.R.S. Nadar, “Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm,” International Journal of Clothing Science and Technology, 2019.
• [17] Z. Cuifang, C. Yu, and M. Jiacheng, “Fabric defect detection algorithm based on PHOG and SVM,” Indian Journal of Fibre & Textile Research (IJFTR), vol. 45, no. 1, pp. 123-126, 2020.