Automated On-Line Fast Detection for Surface Defect of Steel Strip Based on Multivariate Discriminant Function
Abstract
Surface inspection of steel strips is of great importance to improve the quality because it is mainly affected by the defects on the surface. Digital image processing methods have been developed for defect detection for past few years. As to an automated on-line detection system, the research on rapid defect detection is quite significant. In this paper, an approach to detect surface defects of steel strip based on multivariate discriminant function is discussed. By subdividing the images into blocks and extracting related features, tiny defects are effectively detected. With the inspection of the defects, a multivariate discriminant function model has been established. Persuasive experiments results were obtained which prove the feasibility and accuracy of the proposed method. Thus, this research is quite practical and lays a solid foundation for the future study.
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In a large number of instances, it has been possible to localise RoIs with a high degree of accuracy.
“In a large number of instances [3,35,41,42,56,58,61], it has been possible to localise RoIs with a high degree of accuracy.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [35]
Spatial domain techniques are fast and easy to implement compared to wavelet-based methods, and they have been used in most papers where suitability for real-time operation has been reported.
“Spatial domain techniques are fast and easy to implement compared to wavelet-based methods, and they have been used in most of the papers where suitability for real-time operation have also been reported [2,24,26,35,46,58,59,61,76].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [35]
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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
“In a large number of instances [3,35,41,42,56,58,61], it has been possible to localise RoIs with a high degree of accuracy.”
BackgroundMethodA 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
Ai, Y.-H., & Xu, K. (2013) · Journal of Iron and Steel Research International · doi:10.1016/s1006-706x(13)60102-8 · Cited by 53 · 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
Surface defect identification of aluminium strips with non-subsampled shearlet transform
Liu, X., Xu, K., Zhou, P., Zhou, D., & Zhou, Y. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.105986 · Cited by 24 · see citing works
- Authors
- Weiwei, L., Yunhui, Y., Jun, L., Yao, Z., & Hongwei, S.
- Affiliations
- Northeastern University (CN)
- Venue
- 2008 Second International Symposium on Intelligent Information Technology Application
- Publisher
- IEEE
- Published
- 2008-12
- Pages
- 493-497
- OpenAlex
- W2134435244
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 3
- FWCI
- 1.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Intelligent Information Technology Application (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
- steel strip, surface defect detection, online inspection, image block segmentation, feature extraction, automated visual inspection, steel quality control
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