Journal of Soft Computing Paradigm
IRO Journals
Volume 5 — Issue 2 — June 2023

Fabric Fault and Extra Thread Detection using Convolutional Neural Network

https://doi.org/10.36548/jscp.2023.2.005
Sowmiya A
Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, India
Karunamoorthy B
Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, India
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A, Sowmiya, and Karunamoorthy B. 2023. “Fabric Fault and Extra Thread Detection Using Convolutional Neural Network”. Journal of Soft Computing Paradigm 5 (2): 148-63. https://doi.org/10.36548/jscp.2023.2.005.
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Keywords

— Convolution
— ReLU
— Pooling
— Inception Method
— MobileNet.
Published: 21-06-2023

Abstract

A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws in order to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, ReLU, Pooling, Fully Connected layer. A number of well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect are tested in this study. Finally, The study demonstrates the result by classification and proves how accurately the defects are identified.

References

  1. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, ‘‘Image quality assessment: From error visibility to structural similarity,’’ IEEE Trans. Image Process, vol. 13, no. 4, pp. 600–612, Apr. 2004.
  2. M. Patil, S. Verma, and J. Wakode, ‘‘A review on fabric defect detection techniques,’’ Int. Res. J. Eng. Technol, vol. 4, no. 9, pp. 131–136, 2017.
  3. C.-H. Chan and G. K. H. Pang, ‘‘Fabric defect detection by Fourier analy- sis,’’ IEEE Trans. Ind. Appl, vol. 36, no. 5, pp. 1267–1276, Sep./Oct. 2000.
  4. Y. T. Ngan, G. K. H. Pang, and N. H. C. Yung, ‘‘Automated fabric defect detection—A review,’’ Image Vis. Compute, vol. 29, no. 7, pp. 442–458, 2011.
  5. Y. T. Ngan and G. K. H. Pang, ‘‘Regularity analysis for patterned texture inspection,’’ IEEE Trans. Autom. Sci. Eng, vol. 6, no. 1, pp. 131–144, Jan. 2009.
  6. Long, E. Shelhamer, and T. Darrell, ‘‘Fully convolutional networks for semantic segmentation,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, Jun. 2015, pp. 3431–3440.
  7. L. Mak and P. Peng, ‘‘Detecting defects in textile fabrics with optimal Gabor filters,’’ Int. J. Comput. Sci, vol. 1, no. 4, pp. 274–282, 2006.
  8. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, ‘‘Dropout: A simple way to prevent neural networks from overfitting,’’ J. Mach. Learn. Res, vol. 15, no. 1, pp. 1929– 1958, 2014.
  9. F. Bianconi, L. Ceccarelli, A. Fernández, and S. A. Saetta, ‘‘A sequential machine vision procedure for assessing paper impurities,’’ Comput. Ind, vol. 65, no. 2, pp. 325–332, 2014.
  10. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, ‘‘Hypercolumns for object segmentation and fine-grained localization,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, Jun. 2015, pp. 447–456.
  11. He, X. Zhang, S. Ren, and J. Sun, ‘‘Deep residual learning for image recognition,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, Jun. 2016, pp. 770–778.
  12. S. Zheng et al., ‘‘Conditional random fields as recurrent neural networks,’’ in Proc. IEEE Int. Conf. Comput. Vis, Dec. 2015, pp. 1529–1537.
  13. O. Ronneberger, P. Fischer, and T. Brox, ‘‘U-net: Convolutional networks for biomedical image segmentation,’’ in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent, Cham, Switzerland, 2015, pp. 234–241.
  14. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, ‘‘Automatic differentiation in PyTorch,’’ in Proc. 29th Annu. Conf. Neural Inf. Process. Syst. (NIPS), Long Beach, CA, USA, 2017, pp. 1–4.
  15. B. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, ‘‘Rethinking the inception architecture for computer vision,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 2818–2826.