Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features

Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features In this paper, a method combining the use of discrete shearlet transform (DST) and the gray-level co-occurrence matrix (GLCM) is presented to classify surface defects of hot-rolled steel strips into the six classes of rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. Feature extraction involves the extraction of multi-directional shearlet features from each input image followed by GLCM calculations from all extracted sub-bands, from which a set of statistical features is extracted. The resultant high-dimensional feature vectors are then reduced using principal component analysis. A supervised support vector machine classifier is finally trained to classify the surface defects. The proposed feature set is compared against the Gabor, wavelets and the original GLCM in order to evaluate and validate its robustness. Experiments were conducted on a database of hot-rolled steel strips consisting of 1800 grayscale images whose defects exhibit high inter-class similarity as well as high intra-class appearance variations. Results indicate that the proposed DST–GLCM method is superior to other methods and achieves classification rates of 96.00%. Keywords Steel surface classification · Manufacturing defects detection · Discrete shearlet transform · Hot-rolled steel strips · Gray-level co-occurrence matrix · Principal component analysis · Support vector machines 1 Introduction operator. Further http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Arabian Journal for Science and Engineering Springer Journals

Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features

Loading next page...
 
/lp/springer_journal/surface-defects-classification-of-hot-rolled-steel-strips-using-multi-pDUssGEfDb
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by King Fahd University of Petroleum & Minerals
Subject
Engineering; Engineering, general; Science, Humanities and Social Sciences, multidisciplinary
ISSN
1319-8025
eISSN
2191-4281
D.O.I.
10.1007/s13369-018-3329-5
Publisher site
See Article on Publisher Site

Abstract

In this paper, a method combining the use of discrete shearlet transform (DST) and the gray-level co-occurrence matrix (GLCM) is presented to classify surface defects of hot-rolled steel strips into the six classes of rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. Feature extraction involves the extraction of multi-directional shearlet features from each input image followed by GLCM calculations from all extracted sub-bands, from which a set of statistical features is extracted. The resultant high-dimensional feature vectors are then reduced using principal component analysis. A supervised support vector machine classifier is finally trained to classify the surface defects. The proposed feature set is compared against the Gabor, wavelets and the original GLCM in order to evaluate and validate its robustness. Experiments were conducted on a database of hot-rolled steel strips consisting of 1800 grayscale images whose defects exhibit high inter-class similarity as well as high intra-class appearance variations. Results indicate that the proposed DST–GLCM method is superior to other methods and achieves classification rates of 96.00%. Keywords Steel surface classification · Manufacturing defects detection · Discrete shearlet transform · Hot-rolled steel strips · Gray-level co-occurrence matrix · Principal component analysis · Support vector machines 1 Introduction operator. Further

Journal

Arabian Journal for Science and EngineeringSpringer Journals

Published: Jun 5, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off