Skip to main content
SpringerLink
Log in

Automatic Printed Fabric Defect Detection Based on Image Classification Using Modified VGG Network

  • Conference paper
  • First Online:
Advances in Simulation and Digital Human Modeling (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 264))

Included in the following conference series:

Abstract

Fabric defect detection is a critical operation in textile manufacturing. The inaccuracy in manual fabric inspection system may cause high fabric wastage. Therefore, deep learning algorithm-based automatic fabric defect detection (ADD) method is expected to gradually replace manual inspection method. However, the existing literature on ADD shows scarcity in the number of research conducted on printed fabric defect detection. A recent research on ADD of printed fabric showed that Virtual Geometric Group-16 (VGG16) network showed better performance in defect detection than that of a customized convolutional neural network (CNN) [1]. Our research proposes a modified structure of VGG16 and uses an extended printed fabric dataset to identify color spots and misprints on the printed fabric. The results show that proposed methodology enables the model to achieve higher accuracy (76%) than that of the accuracy (62.28%) achieved in the previous research [1]].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chakraborty, S., Moore, M., Parrillo-Chapman, L.: Automatic Defect Detection of Print Fabric Using Convolutional Neural Network, arXiv:2101.00703, Jan 2021. Accessed 16 Jan 2021. http://arxiv.org/abs/2101.00703

  2. Zhu, Z., Han, G., Jia, G., Shu, L.: Modified DenseNet for automatic fabric defect detection with edge computing for minimizing latency. IEEE Internet Things J. 7(10), 9623–9636 (2020). https://doi.org/10.1109/JIOT.2020.2983050

    Article  Google Scholar 

  3. Eldessouki, M.: Computer vision and its application in detecting fabric defects. In: Applications of Computer Vision in Fashion and Textiles, pp. 61–101. Elsevier (2018)

    Google Scholar 

  4. Hanbay, K., Talu, M.F., Özgüven, Ö.F.: Fabric defect detection systems and methods—a systematic literature review. Optik 127(24), 11960–11973 (2016). https://doi.org/10.1016/j.ijleo.2016.09.110

    Article  Google Scholar 

  5. Ngan, H.Y.T., Pang, G.K.H., Yung, N.H.C.: Automated fabric defect detection—a review. Image Vis. Comput. 29(7), 442–458 (2011). https://doi.org/10.1016/j.imavis.2011.02.002

    Article  Google Scholar 

  6. Bandara, P., Bandara, T., Ranatunga, T., Vimarshana, V., Sooriyaarachchi, T., Silva, C.D.: Automated fabric defect detection. In: 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, September 2018, pp. 119–125. https://doi.org/10.1109/ICTER.2018.8615491.

  7. Hanbay, K., Talu, M.F., Özgüven, Ö.F., Öztürk, D.: Real-time detection of knitting fabric defects using shearlet transform, Tekstil ve Konfeksiyon, 29(1) (2019). Art. no. 1. https://doi.org/10.32710/tekstilvekonfeksiyon.482888

  8. Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018). https://doi.org/10.3390/s18041064

    Article  Google Scholar 

  9. . Guan, M., Zhong, Z., Rui, Y.: Automatic defect segmentation for plain woven fabric images. In: 2019 International Conference on Communications, Information System and Computer Engineering (CISCE), Haikou, China, July 2019, pp. 465–468. https://doi.org/10.1109/CISCE.2019.00108.

  10. Calik, R.C., Demirci, M.F.: Cifar-10 image classification with convolutional neural networks for embedded systems. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), Aqaba, October 2018, pp. 1–2. https://doi.org/10.1109/AICCSA.2018.8612873

  11. Czimmermann, T., et al.: Visual-based defect detection and classification approaches for industrial applications—a survey. Sensors 20(5), 1459 (2020). https://doi.org/10.3390/s20051459

    Article  Google Scholar 

  12. Li, Y., Huang, H., Xie, Q., Yao, L., Chen, Q.: Research on a surface defect detection algorithm based on mobilenet-SSD. Appl. Sci. 8(9), 1678 (2018). https://doi.org/10.3390/app8091678

    Article  Google Scholar 

  13. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  14. Kandel, I., Castelli, M., Popovič, A.: Comparative study of first order optimizers for image classification using convolutional neural networks on histopathology images. J. Imaging 6(9), 92 (2020). https://doi.org/10.3390/jimaging6090092

    Article  Google Scholar 

  15. Luo, P., Wang, X., Shao, W., Peng, Z.: Towards Understanding Regularization in Batch Normalization. arXiv:1809.00846, April 2019. Accessed 1 Nov 2020. http://arxiv.org/abs/1809.00846

  16. Szeliski, R.: Computer vision: Algorithms and applications. Springer, London (2011)

    Book  Google Scholar 

  17. Ballard, D.H., Brown, C.M.: Computer Vision. Prentice-Hall, Englewood Cliffs (1982)

    Google Scholar 

  18. Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: 2014 Fourth International Conference on Advanced Computing and Communication Technologies, Rohtak, India, February 2014, pp. 5–12. https://doi.org/10.1109/ACCT.2014.74

  19. Brownlee, J.: A Gentle Introduction to Pooling Layers for Convolutional Neural Networks. Machine learning mastery (2019). https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/. Accessed 12 Mar 2021

  20. Brownlee, J.: A Gentle Introduction to Batch Normalization for Deep Neural Networks. Machine learning mastery (2019). https://machinelearningmastery.com/batch-normalization-for-training-of-deep-neural-networks/. Accessed 12 Mar 2021

  21. Santurkar, S., Tsipras, D., Ilyas, A., Madry, A.: How Does Batch Normalization Help Optimization? arXiv:1805.11604, April 2019. Accessed 12 Mar 2021. http://arxiv.org/abs/1805.11604

  22. Uppal, S.: Curse of Batch Normalization. Towards data science (2020). https://towardsdatascience.com/curse-of-batch-normalization-8e6dd20bc304. Accessed 12 Mar 2021

  23. Doshi, S.: Various Optimization Algorithms For Training Neural Network. Towards data science, 12 March 2021. https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6

  24. Gao, C., Zhou, J., Wong, W.K., Gao, T.: Woven fabric defect detection based on convolutional neural network for binary classification. In: Wong, W.K. (ed.) AITA 2018. AISC, vol. 849, pp. 307–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99695-0_37

    Chapter  Google Scholar 

  25. Missinglink, Convolutional Neural Networks Fully Connected Layers in Convolutional Neural Networks: The Complete Guide, missinglink.ai 2020. https://missinglink.ai/guides/convolutional-neural-networks/fully-connected-layers-convolutional-neural-networks-complete-guide/

  26. LeCun, Y.: LeNet-5, convolutional neural networks. yann.lecun.com (2020). http://yann.lecun.com/exdb/lenet/. Accessed 31 Oct 2020

  27. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), Antalya, August 2017, pp. 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.

  28. Basirat, M., Roth, P.M.: The Quest for the Golden Activation Function. arXiv:1808.00783, August 2018. Accessed 1 Nov 2020. http://arxiv.org/abs/1808.00783

  29. Devarakonda, A., Naumov, M., Garland, M.: AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks. arXiv:1712.02029, February 2018. http://arxiv.org/abs/1712.02029. Accessed 1 Nov 2020

  30. Ide, H., Kurita, T.: Improvement of learning for CNN with ReLU activation by sparse regularization. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, May 2017, pp. 2684–2691.https://doi.org/10.1109/IJCNN.2017.7966185

  31. Park, S., Kwak, N.: Analysis on the dropout effect in convolutional neural networks. In: Lai, S.-H., Lepetit, V., Nishino, Ko., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10112, pp. 189–204. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54184-6_12

    Chapter  Google Scholar 

  32. Takase, T., Oyama, S., Kurihara, M.: Effective neural network training with adaptive learning rate based on training loss. Neural Netw. 101, 68–78 (2018). https://doi.org/10.1016/j.neunet.2018.01.016

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Textile and Apparel, Technology and Management, North Carolina State University, NC, 27695, Raleigh, USA

    Samit Chakraborty, Marguerite Moore & Lisa Parrillo-Chapman

Authors
  1. Samit Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marguerite Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lisa Parrillo-Chapman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samit Chakraborty .

Editor information

Editors and Affiliations

  1. U.S. Army Research Laboratory, Orlando, FL, USA

    Julia L. Wright

  2. Soar Tech, Orlando, FL, USA

    Daniel Barber

  3. Department of Product Development, University of Antwerp, Antwerp, Belgium

    Sofia Scataglini

  4. Biomedical Research and Environmental, NASA Lyndon Johnson Space Center, Houston, TX, USA

    Sudhakar L. Rajulu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chakraborty, S., Moore, M., Parrillo-Chapman, L. (2021). Automatic Printed Fabric Defect Detection Based on Image Classification Using Modified VGG Network. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_46

Download citation

Publish with us

Policies and ethics

Search

Navigation