Detection of Scarfing Faults on the Edges of Slabs
Open accessAbstract
At present, quality control is becoming a major issue in steel production. Thus we developed an algorithm that uses machine vision to detect scarfing faults on slabs, which impairs the steel quality of subsequent products such as steel plates. Scarfing faults typically occur in three locations: the top, middle, and edge of the slab. Our proposed algorithm is focused on detecting scarfing faults on the edge of slab, which is tiny and sometimes indistinct. A machine vision system with a line scan camera was designed, which facilitates the detection of brightness differences and texture differences between well-scarfed and poorly-scarfed slab surface. Scarfing faults are tiny on the edges, so we propose a new segmentation method that takes advantage of capabilities of the line scan camera. A segmented image is filtered using Gabor filters, which were designed to focus on the boundary with scarfing faults to identify specific regions with defect, referred to as defect candidates. Each defect candidate is classified using a Support Vector Machine (SVM) classifier based on its extracted features. Our proposed algorithm was effective according to the experimental trials using 2061 frame images acquired from real samples, where the true detection rate was 97.26% and the false detection rate was 1.66%. Our proposed system and algorithm based on machine vision technology facilitates scarfing faults detection, which can be detected before rolling process, resulting in improved steel quality.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Median filters have the best performance on the suppression of salt and pepper noise, and Gaussian filter and Wiener filter have better effects on Gaussian noise.
“Among them, it is proved that the median filters have the best performance on the suppression of salt and pepper noise, and the Gaussian filter and Wiener filter have better effects on Gaussian noise.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [47]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,070 · see citing works
Comparison of texture features based on Gabor filters
Grigorescu, S. E., Petkov, N., & Kruizinga, P. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.804262 · Cited by 745 · see citing works
The generalized Gabor scheme of image representation in biological and machine vision
Porat, M., & Zeevi, Y. Y. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.3910 · Cited by 447 · see citing works
Applications of Support Vector Machines for Pattern Recognition: A Survey
Byun, H., & Lee, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45665-1_17 · Cited by 352 · see citing works
Self-citationA double-threshold image binarization method based on edge detector
Chen, Q., Sun, Q., Heng, P., et al. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.09.007 · Cited by 125 · see citing works
Texture segmentation using filters with optimized energy separation
Randen, T., & Husøy, J. H. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.753744 · Cited by 88 · see citing works
Real-time vision-based defect inspection for high-speed steel products
Yun, J. P., Choi, S., Seo, B., & Kim, S. W. (2008) · Optical Engineering · doi:10.1117/1.2957958 · Cited by 59 · see citing works
Self-citationSahoo, P. P., Kumar, A., Halder, J., et al. (2009) · ISIJ International · doi:10.2355/isijinternational.49.521 · Cited by 45 · see citing works
Pinhole detection in steel slab images using Gabor filter and morphological features
Choi, D.-C., Jeon, Y.-J., Yun, J. P., & Kim, S. W. (2011) · Applied Optics · doi:10.1364/ao.50.005122 · Cited by 43 · see citing works
Self-citationReal, imaginary, and clutter Gabor filter fusion for detection with reduced false alarms
Casasent, D. P., & Smokelin, J. S. (1994) · Optical Engineering · doi:10.1117/12.172407 · Cited by 39 · see citing works
Blistering Behavior during Oxide Scale Formation on Steel Surface
Kondo, Y., Tanei, H., Suzuki, N., et al. (2011) · ISIJ International · doi:10.2355/isijinternational.51.1696 · Cited by 29 · see citing works
Surface defects inspection method in hot slab continuous casting process
Zhao, L. M., Ouyang, Q., Chen, D. F., et al. (2011) · Ironmaking & Steelmaking Processes Products and Applications · doi:10.1179/1743281211y.0000000025 · Cited by 17 · see citing works
Cho, M. J., Park, E. B., & Kim, S. W. (2010) · ISIJ International · doi:10.2355/isijinternational.50.1180 · Cited by 14 · see citing works
Self-citationProduction and Technology of Iron and Steel in Japan during 2006
Kagechika, H. (2007) · ISIJ International · doi:10.2355/isijinternational.47.773 · Cited by 12 · see citing works
Faulty Scarfing Slab Detection Using Machine Vision
Choi, D.-C., Jeon, Y. J., & Kim, S. W. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.462.185 · Cited by 5 · see citing works
Self-citationOptical Line Scan Inspection System for Pseudo-particle Analysis
Mou, J.-L., Yi-min, S., Chen, Y., et al. (2007) · ISIJ International · doi:10.2355/isijinternational.47.1280 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 27
- H. Kagechika: ISIJ Int., 47 (2007), 773.
- P. P. Sahoo, A. Kumar, J. Halder and M. Raj: ISIJ Int., 49 (2009), 521.
- J. P. Yun, S. H. Choi, B. Seo and S. W. Kim: Opt. Eng., 47 (2008), 077204.
- M. Sharifzadeg, S. Alirezaee, R. Amirfattahi and S. Sadri: 12th IEEE Int. Multitopic Conf., IEEE, New York, (2008), 125.
- L. M. Zhao, Q. Quyang, D. F. Chen and L. Y. Wen: Ironmaking and Steelmaking., 38 (2011), 464.
- M. J. Cho, E. B. Park and S. W. Kim: ISIJ Int., 50 (2010), 1180.
- J. P. Yun, S. H. Choi, J. W. Kim and S. W. Kim: NDT&E Int., 47 (2009), 389.
- D. Choi, Y. J. Jeon and S. W. Kim: Adv. Mater. Res., 462 (2012), 185.
- S. G. Ryu, Y. J. Jeon, D. Choi, J. P. Yun and S. W. Kim: ICROS 28th Annual Conf., ICROS, Buchun, (2013), 278.
- Y. Kondo, H. Tanei, N. Suzuki, K. Ushioda and M. Maeda: ISIJ Int., 51 (2011), 1696.
- K. L. Mak and P. Peng: Robotics and Comput. -Integ. Manuf., 4 (2008), 359.
- J. L. Mou, Y. M. Sun, Y. C. Chen, C. W. Liao and Y. S. Tarng: ISIJ Int., 47 (2007), 1280.
- D. Choi, Y. J. Jeon, J. P. Yun and S. W. K: Appl. Opt., 50 (2011), 5122.
- M. Porat and Y. Y. Zeevi: IEEE Trans. on Pattern Anal. and Mach. Intel., 10 (1988), 452.
- D. Casasent and J. S. Smokelin: Opt. Eng., 33 (1994), 2255.
- P. Kruizinga, N. Petkov and S. E. Grigorescu: IEEE Trans. on Image Process., 11 (2002), 1160.
- G. Kang and H. Liu: Proc. of 4th Int. Conf. on Mach. Learn. and Cybern., IEEE, NewYork, (2005), 5034.
- D. M. Tsai and S. K. Wu: The Int. J. of Adv. Manuf. Tech., 16 (2000), 474.
- Q. Chen, Q. Sun, P. A. Heng and D. Xia: Pattern Recogn., 41 (2008), 1254.
- R. C. Gonzalez and R. E. Wood: Digital Image Processing, Vol. 3, Prentice Hall, New Jersey, (2002), 668.
- A. Bosson, G. C. Cawley, Y. Chan and R. Harvey: Int. Conf. on Image and Video Retrieval, Springer, New York, (2002), 50.
- T. Randen and J. H. Husoy: IEEE Trans. on Image Process., 8 (1999), 571.
- N. Dalal and B. Triggs: Conf. on CVPR, IEEE, New York, (2005), 886.
- C. Cortes and V. N. Vapnik: Mach. Learn., 20 (1995), 283.
- B. Gutschoven and P. Verlinde: Proc. of the 3rd Int. Conf. on Inform. Fusion, IEEE, New York, (2000), THB3/3.
- H. Byun and S. Lee: Pattern Recog. with SVM, 2388 (2002), 213.
- H. Jia, Y. L. Murphey, J. Shi and T. S. Chang: Proc. of 4th Int. Conf. on Pattern Recog., IEEE, New York, (2004), 239.
Cited by
Sorted by citations
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
Steel-surface defect detection using a switching-lighting scheme
Jeon, Y.-J., Choi, D.-C., Lee, S. J., Yun, J. P., & Kim, S. W. (2016) · Applied Optics · doi:10.1364/ao.55.000047 · Cited by 61 · see citing works
Self-citationJeon, Y.-J., Choi, D.-C., Yun, J. P., & Kim, S. W. (2015) · ISIJ International · doi:10.2355/isijinternational.isijint-2015-053 · Cited by 17 · see citing works
Self-citation
- Authors
- Ryu, S.-G., Choi, D.-C., Jeon, Y.-J., Lee, S. J., Yun, J. P., & Kim, S. W.
- Affiliations
- Pohang University of Science and Technology (KR)
- Pohang Iron and Steel (South Korea) (KR)
- Venue
- ISIJ International
- Publisher
- Iron and Steel Institute of Japan
- Published
- 2014
- Volume
- 54
- Issue
- 1
- Pages
- 112-118
- ISSN
- 0915-1559 (ISSN-L)
- 1347-5460
- OpenAlex
- W1984496433
- Cited by
- 15
- References
- 17
- Citations by year
- 2025: 1 · 2023: 1 · 2020: 3 · 2018: 4 · 2017: 1 · 2016: 3 · 2015: 2
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- ISIJ International (publishedVersion)
- Open Access System for Information Sharing (Pohang University of Science and Technology) (submittedVersion)
- Funders
- Ministry of Science, ICT and Future Planning (NIPA-2013-H0203-13-1001)
- National IT Industry Promotion Agency (NIPA-2013-H0203-13-1001)
- 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
- machine vision, line-scan camera, image segmentation, Gabor filters, support vector machine, defect detection, steel quality control, steel plate production
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
