A General Product Identification Method for Mass Customization based on Deep Learning

Chenxiao Lin, Shigeru Fujimura, Wutie Zhou, Haipeng Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Most manufacturing industries are facing changes due to the increasing competition in the global market. Regarding this current situation, manufacturing firms that offer mass customization usually have a competitive edge over counterparts offering generic products. However, the Mass Production-to-Mass Customization (MP-to-MC) transition has brought upon unprecedented challenges to many Small and Medium Enterprises (SMEs). One of the challenges is to identify customized products in harsh production environments. The reason behind this is that identification tags such as barcodes, Quick Response (QR) codes and Radio Frequency Identification (RFID) cannot function in special production processes like heating and dissolution. It is therefore of prime importance to find a solution by devising a product identification method without making use of marks or tags. In the paper, a novel method that using computer vision to identify the customized products in mass customization and a hybrid Convolutional Neural Network (CNN) model are proposed. To illustrate the efficacy of the proposed method, a case study in a shoe-manufacturing company was reported. The results yielded demonstrated that the proposed method is an efficient and economical solution.

Original languageEnglish
Title of host publicationProceeding - 2021 China Automation Congress, CAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781665426473
Publication statusPublished - 2021
Event2021 China Automation Congress, CAC 2021 - Beijing, China
Duration: 2021 Oct 222021 Oct 24

Publication series

NameProceeding - 2021 China Automation Congress, CAC 2021


Conference2021 China Automation Congress, CAC 2021


  • computer vision
  • Convolutional Neural Network (CNN)
  • deep learning
  • mass customization
  • product identification
  • Small and Medium Enterprises (SMEs)

ASJC Scopus subject areas

  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Artificial Intelligence


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