Automated visual classification of frequent defects in flat steel coils
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The Gabor filter has optimal joint localization in both the spatial and spatial-frequency domains.
“the GF has optimal joint localization in both the spatial and spatial-frequency domains [62].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [62]
Gabor filters have difficulty determining the optimal filter parameters and are not robust to rotation invariance.
“Difficulty in determining the optimal filter parameters and not robust to rotation invariance.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [62]
AVI systems relying on multi-classifiers can achieve better classification performance and robustness than those based on individual classifiers.
“AVI systems relying on multi-classifiers can achieve better classification performance and robustness than those based on individual classifiers.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [62]
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Cites
Daugman, J. G. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001160 · Cited by 3,219 · see citing works
An intelligent real-time vision system for surface defect detection
Jia, H., Murphey, Y. L., Shi, J., & Chang, T.-S. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1334512 · Cited by 174 · see citing works
Surface defects inspection of cold rolled strips based on neural network
Kang, G.-W., & Liu, H.-B. (2005) · 2005 International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2005.1527830 · Cited by 62 · see citing works
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Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · see citing works
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
A deep-learning-based approach for fast and robust steel surface defects classification
Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M. Y., & Cao, Y. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.05.005 · Cited by 271 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
A Compact Convolutional Neural Network for Surface Defect Inspection
Huang, Y., Qiu, C., Wang, X., Wang, S., & Yuan, K. (2020) · Sensors · doi:10.3390/s20071974 · Cited by 85 · see citing works
Zhao, Y. J., Yan, Y. H., & Song, K. C. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-016-9489-0 · Cited by 59 · see citing works
Song, K.-C., Hu, S.-P., Yan, Y.-H., & Li, J. (2014) · ISIJ International · doi:10.2355/isijinternational.54.2598 · Cited by 41 · see citing works
- Authors
- Medina, R., Gayubo, F., González-Rodrigo, L. M., Olmedo, D., Gómez-García-Bermejo, J., Zalama, E., & Perán, J. R.
- Affiliations
- CARTIF (ES)
- Universidad de Valladolid (ES)
- Corresponding author
- Medina, Roberto
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2011-05-04
- Volume
- 57
- Issue
- 9-12
- Pages
- 1087-1097
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- OpenAlex
- W2027528832
- Cited by
- 49
- References
- 3
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 7 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 5 · 2015: 2 · 2014: 5 · 2013: 3
- FWCI
- 7.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The International Journal of Advanced Manufacturing Technology (publishedVersion)
- Funders
- Ministerio de Ciencia e Innovación
- 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
- automated visual inspection, surface defect detection, defect classification, feature extraction, weld defects, white rust, pitting corrosion, steel quality control, image acquisition, image processing, steel cutting
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