Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics

Yıl 2022, , 160 - 165, 29.09.2022

Mahdi Hatami Varjovi , Muhammed Fatih Talu , Kazım Hanbay

https://doi.org/10.46810/tdfd.1108264

Cited By: 1

Öz

Visual inspection is a main stage of quality assurance process in many applications. In this paper, we propose a new network architecture for detecting the fabric defects based on convolutional neural network. Four different pre-trained and customized model network architectures have compared in terms of performance. Results has been evaluated on a fabric defect dataset of 13.800 images. Among the existing Inception V3, MobileNetV2, Xception and ResNet50 methods, the InceptionV3 model has achieved 78% classification success. Our designed deep network model could achieve 97% success. The experimental works show that the designed deep model is effective in detecting the fabric defects.

Anahtar Kelimeler

Fabric defect classification, Deep learning, Neural networks, Model optimization

Destekleyen Kurum

The Turkish Scientific and Technological Research Council. (TÜBİTAK)

Proje Numarası

5180054

Kaynakça

• Hanbay K, Talu MF, Özgüven ÖF. Fabric defect detection systems and methods—A systematic literature review. Optik. 2016 Dec 1;127(24):11960–73.
• Mahajan P, Kolhe S R, Patil P M. A review of automatic fabric defect detection techniques. Advances in Computational Research. 2009. 1(2): 18-29.
• Kumar A. Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. 2008.55(1): 348-363.
• Fanga B, Lia Y, Zhanga H,. Chan J C-W. Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples. ISPRS Journal of Photogrammetry and Remote Sensing. 2020.161:164-178.
• Sezer A, Sezer H B. Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction. Ultrasound in Medicine & Biology. 2020. 46(3): 735-749.
• Wei B, Hao K, Tang X, Ding Y. A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes. Textile Research Journal. 2019. 89(17): 3539-3555.
• Zhanga M, Wu J, Lina H, Yuan P, Song Y. The Application of One-Class Classifier Based on CNN in Image Defect Detection. Procedia Computer Science. 2017. 114: 341-348.
• Zhao Y, Hao K, He H, Tang X, Wei B. A visual long-short-term memory based integrated CNN model for fabric defect image classification. Neurocomputing. 2020, 380: 259-270.
• Liu J, Wang C, Su H, Du B, Tao D. Multistage GAN for Fabric Defect Detection. IEEE Transactions on Image Processing. 2019. 29:3388-3400.
• SUN G, ZHOU Z, GAO Y, XU Y, XU L, LIN S. A Fast Fabric Defect Detection Framework for Multi-Layer Convolutional Neural Network Based on Histogram Back-Projection. IEICE Transactions on Information and Systems. 2019. 102(12): 2504-2514.
• Jing J-F, Ma H, Zhang H-H. Automatic fabric defect detection using a deep convolutional neural network. Coloration Technology. 2019. 135(3): 213-223.
• Weimer D, Scholz-Reiter B, Shpitalni M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals - Manufacturing Technology. 2016. 1481:4.
• Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Computer Vision and Pattern Recognition. 2016. 1-10.
• Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR; 2014. p. 1–14.
• Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep con- volutional neural networks. Adv. Neural Inf. Proces. Syst. 2012. 60(6): 1097-1105.
• He K, Zhang X, Ren S, Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. 770-778.
• Hanbay K, Talu M F, Özgüven Ö F, Öztürk D. Fabric defect detection methods for circular knitting machines. 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, 2015. 735-738.
• Hanbay K, Fatih Talu M, Özgüven ÖF, Öztürk D. Real-Time Detection of Knitting Fabric Defects Using Shearlet Transform. Tekst ve Konfeksiyon. 29(1):2019. 3-10.
• Chollet, F. (2015) keras, GitHub. https://github.com/fchollet/keraserences