Abstract
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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International Organization for Standardization, ISO 22005: trace-ability in the feed and food chain — general principles and basic requirements for system design and implementation, 2007. https://www.iso.org/standard/36297.html
C.J. Smith, Ensuring Supply Chain Security: The Role of Anti-Counterfeiting Technologies, 2016. http://www.unicri.it/topics/counterfeiting/anticounterfeiting_technologies/Ensuring_supply_chain_security_report.pdf
S. Tamayo, T. Monterio, N. Sauer, Deliveries optimization by exploiting production traceability information, Eng. Appl. Artif. Intell. 22 (2009), 557–568
A. Abercrombie, E. Alexiev, D. Bral, C. Manager, B. Silvia Bollani, Informal Product Traceability Expert Group, final report, 2013. https://ec.europa.eu/info/sites/info/files/final-report_product-traceability-expert-group_2013_en.pdf
Rapanui Clothing, Traceability for clothing, 2016. https://rapanuiclothing.com/traceability-clothing/
B. Xin, J. Hu, G. Baciu, X. Yu, Development of weave code technology for textile products, Fibres Text. East. Eur. 85 (2011), 33–35. http://www.fibtex.lodz.pl/article497.html
V. Kumar, L. Koehl, X. Zeng, A fully yarn integrated tag for tracking the international textile supply chain, J. Manuf. Syst. 40 (2016), 76–86
V. Kumar, L. Koehl, X. Zeng, D. Ekwall, Coded yarn based tag for tracking textile supply chain, J. Manuf. Syst. 42 (2017), 124–139
T.K. Agrawal, L. Koehl, C. Campagne, A secured tag for implementation of traceability in textile and clothing supply chain, Int. J. Adv. Manuf. Technol. 99 (2018), 2563–2577
V. Kumar, C. Hallqvist, D. Ekwall, Developing a framework for traceability implementation in the textile supply chain, Systems. 5 (2017), 33.
X. Yao, J. Zhou, Y. Lin, Y. Li, H. Yu, Y. Liu, Smart manufacturing based on cyber-physical systems and beyond, J. Intell. Manuf. 24 (2017), 1–13
V. Kumar, Exploring fully integrated textile tags and information systems for implementing traceability in textile supply chains, 2017. http://ori.univ-lille1.fr/notice/view/univ-lille1-ori-454906
T.C.T. Chen, Cloud intelligence in manufacturing, J. Intell. Manuf. 28 (2017), 1057–1059
S. Corbellini, F. Ferraris, M. Parvis, A cryptographic system for brand authentication and material traceability in the textile industry, in 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings, IEEE, Sorrento, 2006, pp. 1331–1335.
K. Kuusk, O. Tomico, G. Langereis, Crafting smart textiles – a meaningful way towards societal sustainability in the fashion field? Nord. Text. J. 1 (2012), 6–15. http://hb.diva-portal.org/smash/get/diva2:869634/FULLTEXT01.pdf
C. Michener, RFID on the factory floor - Protect the brand by integrating RFID at point of manufacture - RFID arena, 2019. https://invengo.com/2015/protect-the-brand-by-integrating-rfid-at-point-of-manufacture/
S. Kubler, W. Derigent, A. Thomas, E. Rondeau, Embedding data on “communicating materials” from context-sensitive information analysis, J. Intell. Manuf. 25 (2014), 1053–1064
A.R. Köhler, L.M. Hilty, C. Bakker, Prospective impacts of electronic textiles on recycling and disposal, J. Ind. Ecol. 15 (2011), 496–511
J. Kirtland, Identification Numbers and Check Digit Schemes, Mathematical Association of America, America, 2001. https://books.google.fr/books?hl=en&lr=&id=Z8eka35WUb8C&oi=fnd&pg=PR9&dq=check+digit&ots=OZcN6li0Hz&sig=kiHACWOcUU94wC6EJTKDtdeceKo#v=onepage&q=checkdigit&f=false
G. Csurka, M. Humenberger, From handcrafted to deep local invariant features, ArXiv e-Prints, America2018. https://arxiv.org/abs/1807.10254
D.G. Lowe, Distinctive image features from scale-invariant key-points, Int. J. Comput Vis. 60 (2004), 91–110
T.M. Mitchell, Machine Learning, McGraw-Hill Science/Engineering/Math, America, 1997. ISBN 0070428077.
G. Anthes, Deep learning comes of age, Commun. ACM. 56 (2013), 13.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, ArXiv e-Prints, 2015.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, F.-F. Li, ImageNet: a large-scale hierarchical image database, in IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009, pp. 248–255.
M. Fu, P. Xu, X. Li, Q. Liu, M. Ye, C. Zhu, Fast crowd density estimation with convolutional neural networks, Eng. Appl. Artif. Intell. 43 (2015), 81–88
L.H.S. Vogado, R.M.S. Veras, F.H.D. Araujo, R.R.V. Silva, R. T. Aires, Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification, Eng Appl. Artif. Intell. 72 (2018), 415–422
K. Ko, K. Sim, Deep convolutional framework for abnormal behavior detection in a smart surveillance system, Eng. Appl. Artif. Intell. 67 (2018), 226–234
GS1, EAN/UPC barcodes, 2019. https://www.gs1.org/standards/barcodes/ean-upc
N. Dalal, B. Triggs, O. Gradients, D. Cordelia, N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diago, 2005, pp. 886–893.
B. Herbert, T. Tuytelaars, L. V. Gool, SURF: speeded up robust features, European Conference on Computer Vision, Springer, Berlin, Heidelberg, 2006.
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, Lake Tahoe, 2012, pp. 1–9.
M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes Computer Science, vol. 8689, Springer, Switzerland, 2014, pp. 818–833.
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards realtime object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017), 1137–1149
R. Girshick, J. Donahue, T. Darrell, U.C. Berkeley, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in The IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014, pp. 580–587.
R. Girshick, Fast R-CNN, in The IEEE International Conference on Computer Vision, Santiago, 2015, pp. 1440–1448.
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, A survey on deep transfer learning, in International Conference on Artificial Neural Networks, Rhodes, Greece, 2018, pp. 270–279.
S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowledge Data Eng. 22 (2010), 1345–1359
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks? in Advances in Neural Information Processing Systems, Montréal, Canada, 2014, pp. 3320–3328. https://arxiv.org/abs/1411.1792
M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman, The pascal visual object classes (VOC) challenge, Int. J. Comput. Vis. 88 (2010), 303–338
J. Hosang, R. Benenson, B. Schiele, Learning non-maximum suppression, in Proceeding - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, 2017, pp. 6469–6477.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differentiation in PyTorch, in Advances in Neural Information Processing Systems, Long Beach, CA, 2017, pp. 1–4. https://openreview.net/forum?id=BJJsrmfCZ
J. Yang, J. Lu, B. Dhruv, P. Devi, A faster pytorch implementation of faster R-CNN, 2017. https://github.com/jwyang/faster-rcnn.pytorch
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Wang, K., Kumar, V., Zeng, X. et al. Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning. Int J Comput Intell Syst 12, 713–722 (2019). https://doi.org/10.2991/ijcis.d.190704.002
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DOI: https://doi.org/10.2991/ijcis.d.190704.002