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

Abstract

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.

Keywords

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

Kaynakça

1. Hanbay K, Talu MF, Özgüven ÖF. Fabric defect detection systems and methods—A systematic literature review. Optik. 2016 Dec 1;127(24):11960–73.
2. 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.
3. Kumar A. Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. 2008.55(1): 348-363.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.

9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. Chollet, F. (2015) keras, GitHub. https://github.com/fchollet/keraserences