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A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern

Author

Listed:
  • Shuo Meng

    (Jiangnan University)

  • Ruru Pan

    (Jiangnan University)

  • Weidong Gao

    (Jiangnan University)

  • Jian Zhou

    (Jiangnan University)

  • Jingan Wang

    (Jiangnan University)

  • Wentao He

    (Jiangnan University)

Abstract

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless device. The dataset contains wide kinds of fabrics and detailed fabric structure parameters. Then, a novel multi-task and multi-scale convolutional neural network (MTMSnet) is proposed to predict the location maps of yarns and floats. By adopting the multi-task structure, the MTMSnet can better learn the related features between yarns and floats. Finally, the weave pattern and basic weave repeat are recognized by combining the yarn and float location maps. Extensive experimental results on various kinds of fabrics indicate that the proposed method achieves high accuracy and quality in weave pattern recognition.

Suggested Citation

  • Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01607-9
    DOI: 10.1007/s10845-020-01607-9
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    References listed on IDEAS

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    1. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
    2. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    3. Pedro Malaca & Luis F. Rocha & D. Gomes & João Silva & Germano Veiga, 2019. "Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 351-361, January.
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    Cited by:

    1. Hao Jiang & Zhibin Zhao & Zilong Zhang & Xingwu Zhang & Chenxi Wang & Xuefeng Chen, 2025. "Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2695-2707, April.
    2. Yilin Li & Chengbo Yi & Jianwen Feng & Jingyi Wang, 2022. "Event-Based Impulsive Control for Heterogeneous Neural Networks with Communication Delays," Mathematics, MDPI, vol. 10(24), pages 1-16, December.
    3. Ruining Tang & Zhenyu Liu & Yiguo Song & Guifang Duan & Jianrong Tan, 2024. "Hierarchical multi-scale network for cross-scale visual defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1141-1157, March.
    4. Zichen Bai & Junfeng Jing, 2024. "Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3315-3330, October.
    5. Yuanzhao Shao & Yonghong Song, 2025. "CENet: improve counting performance of X-ray surface mounted chip counter via scale favor and cell extraction," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 303-317, January.
    6. Jie Zhang & Pengpeng Yao & Hochung Wu & John H. Xin, 2023. "Automatic color pattern recognition of multispectral printed fabric images," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2747-2763, August.

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