Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

Chao Yung Hsu; Li Wei Kang; Hsin Yi Lin; Ru Hong Fu; Chih Yang Lin; Ming Fang Weng; Duan Yu Chen

doi:10.1109/ICASI.2018.8394552

Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

Chao Yung Hsu, Li Wei Kang, Hsin Yi Lin, Ru Hong Fu, Chih Yang Lin, Ming Fang Weng, Duan Yu Chen

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

Original language	English
Title of host publication	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	145-146
Number of pages	2
ISBN (Electronic)	9781538643426
DOIs	https://doi.org/10.1109/ICASI.2018.8394552
State	Published - 22 Jun 2018
Event	4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan Duration: 13 Apr 2018 → 17 Apr 2018

Publication series

Name	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference	4th IEEE International Conference on Applied System Innovation, ICASI 2018
Country/Territory	Japan
City	Chiba
Period	13/04/18 → 17/04/18

Keywords

depth image
identification tracking
Industry 4.0
smart manufacturing
steel 4.0
steel industry

Access to Document

10.1109/ICASI.2018.8394552

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Hsu, C. Y., Kang, L. W., Lin, H. Y., Fu, R. H., Lin, C. Y., Weng, M. F., & Chen, D. Y. (2018). Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. In A. D. K.-T. Lam, S. D. Prior, & T.-H. Meen (Eds.), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018 (pp. 145-146). (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2018.8394552

Hsu, Chao Yung ; Kang, Li Wei ; Lin, Hsin Yi et al. / Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 145-146 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

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title = "Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0",

abstract = "To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.",

keywords = "depth image, identification tracking, Industry 4.0, smart manufacturing, steel 4.0, steel industry",

author = "Hsu, {Chao Yung} and Kang, {Li Wei} and Lin, {Hsin Yi} and Fu, {Ru Hong} and Lin, {Chih Yang} and Weng, {Ming Fang} and Chen, {Duan Yu}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Applied System Innovation, ICASI 2018 ; Conference date: 13-04-2018 Through 17-04-2018",

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Hsu, CY, Kang, LW, Lin, HY, Fu, RH, Lin, CY, Weng, MF & Chen, DY 2018, Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 145-146, 4th IEEE International Conference on Applied System Innovation, ICASI 2018, Chiba, Japan, 13/04/18. https://doi.org/10.1109/ICASI.2018.8394552

Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. / Hsu, Chao Yung; Kang, Li Wei; Lin, Hsin Yi et al.
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 145-146 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

AU - Hsu, Chao Yung

AU - Kang, Li Wei

AU - Lin, Hsin Yi

AU - Fu, Ru Hong

AU - Lin, Chih Yang

AU - Weng, Ming Fang

AU - Chen, Duan Yu

PY - 2018/6/22

Y1 - 2018/6/22

N2 - To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

AB - To achieve smart manufacturing in Industry 4.0 for steel industry, a smart steel manufacturing framework is considered in this paper, where an automatic identification tracking method for steel products is developed. Existing approaches usually rely on marking or embedding a series of identification codes on the steel surfaces. However, steel-making is usually processed under a very high temperature environment, making it difficult to well embed the identification codes with acceptable quality for further online processing. Therefore, this paper presents a vision-based automatic identification tracking method without needing to embed any identification codes onto the steel product surfaces. The key is to use the essential identity of a steel product without extrinsic information embedded, achieved by extracting visual features from the steel image. Our preliminary results have verified the efficiency of the proposed method.

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KW - identification tracking

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KW - smart manufacturing

KW - steel 4.0

KW - steel industry

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U2 - 10.1109/ICASI.2018.8394552

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AN - SCOPUS:85050307929

T3 - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

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BT - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

A2 - Lam, Artde Donald Kin-Tak

A2 - Prior, Stephen D.

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Applied System Innovation, ICASI 2018

Y2 - 13 April 2018 through 17 April 2018

ER -

Hsu CY, Kang LW, Lin HY, Fu RH, Lin CY, Weng MF et al. Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0. In Lam ADKT, Prior SD, Meen TH, editors, Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 145-146. (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). doi: 10.1109/ICASI.2018.8394552

Depth-based feature extraction-guided automatic identification tracking of steel products for smart manufacturing in steel 4.0

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