Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning - Université de Lille
Article Dans Une Revue International Journal of Computational Intelligence Systems Année : 2019

Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning

Résumé

The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today's global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement. The traceability coding tag consists of unique features for identification, which links the product with traceability information, plays an important part in the traceability system. In this paper, we describe an innovative technique of product component-based traceability which demonstrates that product's inherent features—extracted using deep learning—can be used as a traceability signature. This has been demonstrated on textile fabrics, where Faster region-based convolutional neural network (Faster R-CNN) has been introduced with transfer learning to provide a robust end-to-end solution for coded yarn recognition. The experimental results show that the deep learning-based algorithm is promising in coded yarn recognition, which indicates the feasibility for industrial application.
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hal-04480159 , version 1 (27-02-2024)

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Kaichen Wang, Vijay Kumar, Xianyi Zeng, Ludovic Koehl, Xuyuan Tao, et al.. Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning. International Journal of Computational Intelligence Systems, 2019, International Journal of Computational Intelligence Systems, 12, pp.713-722. ⟨10.2991/ijcis.d.190704.002⟩. ⟨hal-04480159⟩

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