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An Empirical Study on Fabric Defect Classification Using Deep Network Models

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Future Data and Security Engineering (FDSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

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Abstract

Fabric defect inspection plays an essential role in the textile manufacturing process. Traditional detection is carried out using defect visualization. This method obviously is inefficient in both accuracy and inspection time. Automatic detection, which is based on image processing and machine learning, has been proven to be a suitable approach for this problem. However, due to the variety of defect kinds in a broad range of deferent fabrics, existing methods are actually proposed for typical group of fabric defects. This paper aims to investigate models of deep neural network for the general fabric classification problem. In particular, two models including VGG16 and Darknet are used to classify the defect fabric categories. The models are tested for the TILDA database to evaluate their performances.

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Notes

  1. 1.

    https://www.iigm.in.

  2. 2.

    https://www.cs.toronto.edu/~frossard/post/vgg16.

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Acknowledgment

This work is funded by Saigon University, Ho Chi Minh City, Vietnam under the grant number [CS2018-68] (project contract No. 891/HD-QPTKHCN, dated 26/7/2018).

Author information

Authors and Affiliations

  1. Faculty of Electronics and Telecomminications, Saigon University, 273 An Duong Vuong, District 5, Ho Chi Minh City, Vietnam

    Nguyen Thi Hong Anh & Bui Cong Giao

Authors
  1. Nguyen Thi Hong Anh
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  2. Bui Cong Giao
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Corresponding author

Correspondence to Nguyen Thi Hong Anh .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Anh, N.T.H., Giao, B.C. (2019). An Empirical Study on Fabric Defect Classification Using Deep Network Models. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_54

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

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