Vision-based detection of steel billet surface defects via fusion of multiple image features

Chao Yung Hsu; Li Wei Kang; Chih Yang Lin; Chia Hung Yeh; Chia Tsung Lin

doi:10.3233/978-1-61499-484-8-1239

Vision-based detection of steel billet surface defects via fusion of multiple image features

Chao Yung Hsu, Li Wei Kang, Chih Yang Lin, Chia Hung Yeh, Chia Tsung Lin

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

3 Citations (Scopus)

Abstract

Automatic inspection techniques have been widely employed to achieve high productivity while ensuring high-quality products in steel making industry. In this paper, a vision-based detection framework for automatically detecting different types of steel billet surface defects is proposed. The defects considered in this study includes cratches, corner cracks, sponge cracks, slivers, and roll marks. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-based image enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-based image feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
Editors	William Cheng-Chung Chu, Han-Chieh Chao, Stephen Jenn-Hwa Yang
Publisher	IOS Press BV
Pages	1239-1247
Number of pages	9
ISBN (Electronic)	9781614994831
DOIs	https://doi.org/10.3233/978-1-61499-484-8-1239
Publication status	Published - 2015
Externally published	Yes
Event	International Computer Symposium, ICS 2014 - Taichung, Taiwan Duration: 2014 Dec 12 → 2014 Dec 14

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	274
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Other

Other	International Computer Symposium, ICS 2014
Country/Territory	Taiwan
City	Taichung
Period	2014/12/12 → 2014/12/14

Keywords

defect detection
discrete wavelet transform
feature fusion
high dynamic range
region of interest
steel billet

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.3233/978-1-61499-484-8-1239

Cite this

Hsu, C. Y., Kang, L. W., Lin, C. Y., Yeh, C. H., & Lin, C. T. (2015). Vision-based detection of steel billet surface defects via fusion of multiple image features. In W. C.-C. Chu, H.-C. Chao, & S. J.-H. Yang (Eds.), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (pp. 1239-1247). (Frontiers in Artificial Intelligence and Applications; Vol. 274). IOS Press BV. https://doi.org/10.3233/978-1-61499-484-8-1239

Vision-based detection of steel billet surface defects via fusion of multiple image features. / Hsu, Chao Yung; Kang, Li Wei; Lin, Chih Yang et al.
Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. ed. / William Cheng-Chung Chu; Han-Chieh Chao; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. p. 1239-1247 (Frontiers in Artificial Intelligence and Applications; Vol. 274).

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

Hsu, CY, Kang, LW, Lin, CY, Yeh, CH & Lin, CT 2015, Vision-based detection of steel billet surface defects via fusion of multiple image features. in WC-C Chu, H-C Chao & SJ-H Yang (eds), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. Frontiers in Artificial Intelligence and Applications, vol. 274, IOS Press BV, pp. 1239-1247, International Computer Symposium, ICS 2014, Taichung, Taiwan, 2014/12/12. https://doi.org/10.3233/978-1-61499-484-8-1239

Hsu CY, Kang LW, Lin CY, Yeh CH, Lin CT. Vision-based detection of steel billet surface defects via fusion of multiple image features. In Chu WCC, Chao HC, Yang SJH, editors, Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. IOS Press BV. 2015. p. 1239-1247. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-484-8-1239

Hsu, Chao Yung ; Kang, Li Wei ; Lin, Chih Yang et al. / Vision-based detection of steel billet surface defects via fusion of multiple image features. Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. editor / William Cheng-Chung Chu ; Han-Chieh Chao ; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. pp. 1239-1247 (Frontiers in Artificial Intelligence and Applications).

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abstract = "Automatic inspection techniques have been widely employed to achieve high productivity while ensuring high-quality products in steel making industry. In this paper, a vision-based detection framework for automatically detecting different types of steel billet surface defects is proposed. The defects considered in this study includes cratches, corner cracks, sponge cracks, slivers, and roll marks. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-based image enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-based image feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method.",

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author = "Hsu, {Chao Yung} and Kang, {Li Wei} and Lin, {Chih Yang} and Yeh, {Chia Hung} and Lin, {Chia Tsung}",

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AB - Automatic inspection techniques have been widely employed to achieve high productivity while ensuring high-quality products in steel making industry. In this paper, a vision-based detection framework for automatically detecting different types of steel billet surface defects is proposed. The defects considered in this study includes cratches, corner cracks, sponge cracks, slivers, and roll marks. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-based image enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-based image feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method.

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Vision-based detection of steel billet surface defects via fusion of multiple image features

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