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

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.

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

“…It is stated that this work is part of a broader project aiming to provide a low-cost biometric identification tool to be used along the entire lobster supply chain. Wang ( 2020 ) presented a classification method for online tourism products that provides a reference for developing a personalized data-driven recommendation system in the tourism supply chain. The method analyzes the information of user reviews and tourism products/services reviews to generate personalized recommendation lists.…”

“…The ability to track product flow throughout the supply chain is defined as supply chain traceability (Roth et al 2008 ). In the textile industry, Wang et al ( 2019 ) used DL for the traceability of fabrics. Their proposed technique captures the visual features of textile yarns and transforms them into a traceability signature that can be recognized as a tag and used for product classification.…”

“…In comparison with DNNs, CNNs have some advantages such as more similarity to the human visual processing system, having a powerful structure for 2D and 3D image processing, and having effective performance in extracting and learning abstractions of complex 2D features (Alom et al 2019 ). Wang et al ( 2018 ) briefly described the construction of CNNs as follows: “CNN learns features by the use of alternating and stacking convolutional layers as well as pooling operations. The convolutional layers use several local kernel filters to convolve with raw input data, and then next pooling layers extract the most influential features with an over-sliding window of the raw input data through pooling operations.…”

“…Kotsiopoulos et al ( 2021 ) described the frequently used machine learning and DL algorithms in the industry 4.0 era, focusing on their use in the smart manufacturing and the smart grid field. Wang et al ( 2018 ) comprehensively reviewed DL techniques, their pros and cons, and their applications in smart manufacturing.…”

“…However, they did not discuss the applications and research gaps of evolutionary deep learning in the supply chain area Nti et al ( 2021 ) 2021 Applications and advancements of AI in engineering and manufacturing 2006–2020 Systematic review Their review did not consider all domains and functions of the supply chain. They focused on manufacturing and investigated all AI techniques Kotsiopoulos et al ( 2021 ) 2021 Applications of ML and DL in industry 4.0 1985–2020 Survey Although they reviewed the applications of ML and DL in industry 4.0 including smart supply chains, the main focus of their work is on smart manufacturing and smart grid Wang et al ( 2018 ) 2018 DL methods and their applications in smart manufacturing 1986–2018 Survey The authors focused on manufacturing and other supply chain functions were not included …”