An automated web surface inspection for hot wire rod using undecimated wavelet transform and support vector machine
Abstract
This paper presents defect detection and classification method for hot wire rod in the steel industry. The detection algorithm is based on undecimated discrete wavelet transform (UDWT). The algorithm utilizes the translation invariant property of UDWT. To discriminate the real defects and pseudo defects, we use support vector machine (SVM). The total 14 feature attributes are extracted from binary and gray image. To select best model for SVM classier, we search the parameter spaces by exponentially growing sampling test. The experimental results show the proposed methods can be applied to real-world application.
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For a round product, at least three cameras are used, while use of five cameras has been reported.
“For example, for a round product, at least three cameras are used while use of five cameras has been reported in the literature [5].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [5]
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 [5]
At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation.
“At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation [5,44,58,59].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [5]
In [5], defects are classified from pseudo defects by using SVM classifier with about 92% accuracy in rods.
“After obtaining 14 features from binary and gray images, defects are classified from pseudo defects by using SVM classifier with about 92% accuracy in rods.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [5]
SVM has been widely used for separation of defects from pseudo defects in steel surfaces.
“Thus, it has been widely used for separation of defects from pseudo defects in steel surfaces [2,5,44,47,65,67].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [5]
Cited by
Sorted by citations
Review of vision-based steel surface inspection systems
Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-50 · Cited by 322 · see citing works
Wang, Y., Xia, H., Yuan, X., Li, L., & Sun, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5238-0 · Cited by 61 · see citing works
Real-time system for automatic cold strip surface defect detection
Kostenetskiy, P., Alkapov, R., Vetoshkin, N., Chulkevich, R., Napolskikh, I., & Poponin, O. (2019) · FME Transactions · doi:10.5937/fmet1904765k · Cited by 23 · see citing works
- Authors
- Park, C., & Won, S.
- Affiliations
- Pohang University of Science and Technology (KR)
- ORCID
- Won, SangChul
- Venue
- 2009 35th Annual Conference of IEEE Industrial Electronics
- Publisher
- IEEE
- Published
- 2009-11
- Pages
- 2411-2415
- ISSN
- 1553-572X (ISSN-L)
- OpenAlex
- W2542054509
- Cited by
- 10
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 2 · 2019: 1 · 2018: 1 · 2017: 1 · 2014: 1 · 2012: 1
- FWCI
- 1.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual Conference of the IEEE Industrial Electronics Society (IECON) (publishedVersion)
- 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 › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- surface defect detection, defect classification, undecimated discrete wavelet transform, support vector machine, translation invariance, image feature extraction, steel industry, hyperparameter optimization
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