Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling
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The problem of uneven intensity in rod/bar is reduced to some extent by using more cameras than strictly required.
“This problem is reduced to some extent by using more number of cameras than strictly required as in [5,44] (five cameras) and [85] (four cameras).”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [85]
PK-MSVM was shown to perform better than other multiple SVM algorithms (one against one, one against all and Hastie's algorithm).
“PK-MSVM was shown to perform better than other multiple SVM algorithms (one against one, one against all and Hastie's algorithm).”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [85]
Multiclass problem solution using SVM has been reported in [25,70,81,83-85].
“Multiclass problem solution using SVM has been reported in [25,70,81,83-85].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [85]
Gaussian radial basis function (RBF) kernel function was found to be routinely used for steel surface defect classification.
“Gaussian radial basis function (RBF) kernel function was found to be routinely used for steel surface defect classification [2,83,85].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [85]
Hastie's algorithm was found to perform better than one against one or one against all approach.
“Further, Hastie's algorithm was found to perform better than one against one or one against all approach.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [85]
Agarwal et al. proposed a process-knowledge-based multiclass SVM combining feature extraction with process knowledge; the PK-MSVM performed better than the traditional multiclass SVM.
“Inspired by [59], Agarwal et al. [119] proposed a classification scheme of the process-knowledge-based multiclass SVM (called PK-MSVM) by combining the feature extraction task of the automated defect inspection with the process knowledge; the PK-MSVM performed better than the traditional multiclass SVM.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [119]
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- Authors
- Agarwal, K., Shivpuri, R., Zhu, Y., Chang, T.-S., & Huang, H.
- Affiliations
- The Ohio State University (US)
- OG Technologies (United States) (US)
- Corresponding author
- Shivpuri, Rajiv
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2011-06
- Volume
- 38
- Issue
- 6
- Pages
- 7251-7262
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2000217132
- Cited by
- 54
- References
- 30
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 6 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 1 · 2018: 1 · 2017: 6 · 2016: 2 · 2015: 1 · 2014: 5 · 2013: 3 · 2012: 4
- FWCI
- 7.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- the National Science Foundation (IIP-0646502)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- surface defect classification, steel rolling, process knowledge, multi-class support vector machine, automated inspection, feature extraction, defect localization, one-against-one, one-against-all
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