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 proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
PublisherIOS Press
Pages1239-1247
Number of pages9
ISBN (Electronic)9781614994831
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 2014 Dec 122014 Dec 14

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume274
ISSN (Print)0922-6389

Other

OtherInternational Computer Symposium, ICS 2014
Country/TerritoryTaiwan
CityTaichung
Period2014/12/122014/12/14

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence

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