Development of Defect Classification Algorithm for POSCO Rolling Strip Surface Inspection System
Abstract
Surface inspection system (SIS) is an integrated hardware-software system which automatically inspects the surface of the steel strip. It is equipped with several cameras and illumination over and under the steel strip roll and automatically detects and classifies defects on the surface. The performance of the inspection algorithm plays an important role in not only quality assurance of the rolled steel product, but also improvement of the strip production process control. Current implementation of POSCO SIS has good ability to detect defects, however, classification performance is not satisfactory. In this paper, we introduce POSCO SIS and suggest a new defect classification algorithm which is based on support vector machine technique. The suggested classification algorithm shows good classification ability and generalization performance
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Six defect types from cold strips were correctly classified with 87% to 94% accuracy.
“Six defect types from cold strips were correctly classified with 87% to 94% accuracy.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [83]
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 [83]
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 [83]
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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
“Multiclass problem solution using SVM has been reported in [25,70,81,83-85].”
BackgroundMethodInfluentialA cost-effective and automatic surface defect inspection system for hot-rolled flat steel
Luo, Q., & He, Y. (2016) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2015.09.008 · Cited by 129 · see citing works
Research Progress of Visual Inspection Technology of Steel Products—A Review
Sun, X., Gu, J., Tang, S., & Li, J. (2018) · Applied Sciences · doi:10.3390/app8112195 · Cited by 109 · 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
Defect detection of steel surface using entropy segmentation
Nand, G. K., Noopur, & Neogi, N. (2014) · 2014 Annual IEEE India Conference (INDICON) · doi:10.1109/indicon.2014.7030439 · Cited by 44 · see citing works
Image data assessment approach for deep learning-based metal surface defect-detection systems
Lin, H.-I., & Wibowo, F. S. (2021) · IEEE Access · doi:10.1109/access.2021.3068256 · Cited by 43 · see citing works
- Authors
- Choi, K., Koo, K., & Lee, J.
- Affiliations
- Pohang University of Science and Technology (KR)
- ORCID
- Lee, Jin
- Venue
- 2006 SICE-ICASE International Joint Conference
- Publisher
- IEEE
- Published
- 2006
- Pages
- 2499-2502
- OpenAlex
- W2029669066
- Cited by
- 30
- References
- 0
- Citations by year
- 2024: 2 · 2023: 3 · 2022: 5 · 2021: 1 · 2020: 4 · 2018: 2 · 2017: 2 · 2015: 2 · 2014: 2 · 2013: 1
- FWCI
- 1.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- SICE-ICASE International Joint Conference (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
- defect classification, steel strip, rolling mill, support vector machine, defect detection, image-based inspection, quality assurance
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