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Woven Fabric Defect Detection Based on Convolutional Neural Network for Binary Classification

  • Can Gao
  • Jie Zhou
  • Wai Keung Wong
  • Tianyu Gao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

Fabric defect detection plays an important role in the textile industry. However, this problem is very challenging because of the variability of texture and diversity of defect. In this paper, we investigate the problem of woven fabric defect detection using deep learning. A convolutional neural network with multi-convolution and max-pooling layers is proposed. Moreover, a high-quality database, which covers the common defects in woven fabric with solid color, is built. The experiments conducted on the database indicate that the proposed model could obtain the overall detection accuracy 96.52%, which shows the potential of the model in practical application.

Keywords

Woven fabric defect Deep learning Convolutional neural network Binary classification 

Notes

Acknowledgements

This research is supported by the General Research Fund of the Research Grant Council, Hong Kong (Project No.: 15202217), the Guangdong Natural Science Foundation (Project No.:2018A030310451, 2018A030310450).

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Can Gao
    • 1
    • 2
  • Jie Zhou
    • 1
    • 2
  • Wai Keung Wong
    • 2
  • Tianyu Gao
    • 3
  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China
  2. 2.Institute of Textiles and Clothing, The Hong Kong Polytechnic UniversityKowloonHong Kong
  3. 3.School of Minerals Processing and BioengineeringCentral South UniversityChangshaPeople’s Republic of China

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