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An Automatic Fabric Defect Detector Using an Efficient Multi-scale Network

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14327))

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Abstract

Efficient and accurate fabric defect detection can be beneficial to enhance the competitiveness of enterprises. Aiming at fabric defects with multi-scale characteristics, an Efficient Multi-scale Detector (EMSD) is proposed in this paper. Specifically, by combining Self-calibrated Convolution (SCConv) and Ghost Convolution (GhostConv), a novel feature extraction network is proposed to extract low-level spatial feature maps more accurately and efficiently. Then, a Dense-connected Spatial Pyramid Pooling - Fast (DCSPPF) module is designed to integrate local and global information of low-level spatial feature maps in a way that reduces the loss of defect information. Further, a feature fusion network is constructed to extract high-level semantic feature maps and integrate them with low-level spatial feature maps by skip connections to guide defects localization. Finally, three defect feature maps of different scales are sent into detection heads for large, medium and small defects detection respectively. Experiments are conducted on public Tianchi dataset and TILDA dataset to evaluate the effectiveness of EMSD. The results show that EMSD significantly outperforms all its variants and previous works with a more lightweight network architecture, and has better fabric defect detection capability.

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Acknowledgements

This work is being supported by the National Key Research and Development Project of China under Grant No. 2020AAA0104001and the Zhejiang Provincial Science and Technology Planning Key Project of China under Grant No. 2022C01120.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Fei Gao & Xiaolu Cao

  2. Xinfengming Group Co., Ltd., Tongxiang, 314513, China

    Yaozhong Zhuang

Authors
  1. Fei Gao
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  2. Xiaolu Cao
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  3. Yaozhong Zhuang
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Corresponding author

Correspondence to Fei Gao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gao, F., Cao, X., Zhuang, Y. (2024). An Automatic Fabric Defect Detector Using an Efficient Multi-scale Network. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14327. Springer, Singapore. https://doi.org/10.1007/978-981-99-7025-4_4

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  • DOI: https://doi.org/10.1007/978-981-99-7025-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7024-7

  • Online ISBN: 978-981-99-7025-4

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