Vision-Based Automatic Identification Tracking of Steel Products for Intelligent Steel Manufacturing

Chao Yung Hsu, Li Wei Kang, Teng Yi You, Wei Chen Jhong

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

4 Citations (Scopus)

Abstract

To achieve intelligent manufacturing in Industry 4.0 for steel industry, this paper considers a smart steel manufacturing framework, where an automatic identification tracking method for steel products is proposed. Automatically online tracking and identifying steel products on a production line is essential for intelligent manufacturing management since those products would be frequently moved and processed at different locations on the product flow. Current approaches usually rely on embedding or marking a series of characters (or identification codes) on the steel surface. However, steel-making process is usually conducted under a high temperature environment, making it difficult to well embed the identification codes with high quality and further automatically well recognize them online. To tackle the problem, this paper presents a novel vision-based automatic identification tracking framework without needing to embed any identification codes into the steel product. The key is to employ the essential identity of a steel product without any extrinsic information embedded, achieved by extracting visual features from the product. The presented preliminary results have verified the efficiency of the proposed method, which will be further improved in the future.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-377
Number of pages2
ISBN (Electronic)9781538629369
DOIs
Publication statusPublished - 2017 Dec 28
Externally publishedYes
Event19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan
Duration: 2017 Dec 112017 Dec 13

Publication series

NameProceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
Volume2017-January

Other

Other19th IEEE International Symposium on Multimedia, ISM 2017
Country/TerritoryTaiwan
CityTaichung
Period2017/12/112017/12/13

Keywords

  • feature extraction
  • identification tracking
  • Industry 4.0
  • intelligent manufacturing
  • steel industry

ASJC Scopus subject areas

  • Media Technology
  • Sensory Systems

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