Title :

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

Author(s) :

Wang, Kaichen [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Soochow University
École nationale supérieure des arts et industries textiles [ENSAIT]
Kumar, Vijay [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Koehl, Ludovic [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Tao, Xuyuan [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Chen, Yan [Auteur]

Journal title :

International Journal of Computational Intelligence Systems

Abbreviated title :

Int. J. Comput. Intell. Syst.

Volume number :

12

Pages :

713-722

Publication date :

2019-09-14

ISSN :

1875-6891

English keyword(s) :

Traceability
Textile tags
Coded yarn recognition
Deep learning
Transfer learning
Convolutional neural network

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:14:18Z
2024-02-27T14:10:03Z