The DOI matches its Crossref record; 7 of the 8 references it cites were found in a registry or an index.
An intelligent real-time vision system for surface defect detection
Abstract
In recent years, there is an increased need for quality control in the manufacturing sectors. In the steel making, the rolling operation is often the last process that significantly affects the bulk microstructure of the steel. The cost of having defects on rolled steel is high because it takes more than 5000 KW-Hr to produce a ton of steel. Early detection of defects can reduce product damage and manufacturing cost. This paper describes a real-time visual inspection system that uses support vector machine to automatically learn complicated defect patterns. Based on the experimental results generated from over one thousand images, the proposed system is found to be effective in detecting steel surface detects. The speed of the system for feature extraction and defect detection is less than 6 msec per one-megabyte image.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For long products, particularly wire rods, speed could be as high as 225 miles/h (100 m/s) [2].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
In the citing paper
“First-order gradient filter followed by thresholding is more commonly used for edge localisation [2,44,46,59,76] due to its noise immunity characteristics compared to Laplacian.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
In the citing paper
“Region growing has been employed [2,24,59,76], particularly after the use of gradient operators, to identify RoIs.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
In the citing paper
“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 [2]
In the citing paper
“However, in [2], the use of horizontal gradient operator for detection of vertical edge of seam defects also simultaneously detected equal number of pseudo defects.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
In the citing paper
“Based on seam defect characteristics being very thin and darker than background, seed points were identified and subsequent region growing step provided RoIs.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
In the citing paper
“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 [2]
In the citing paper
“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 [2]
In the citing paper
“Subsequently, accuracy from SVM classifier was found to be better than NN-BP classifier (94.4% against 90.8%) for a 306 test dataset for seam defect against pseudo defect for rod surface.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [2]
Citations
Cites
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Intrusion detection using neural networks and support vector machines
Mukkamala, S., Janoski, G., & Sung, A. H. (2003) · doi:10.1109/ijcnn.2002.1007774 · Cited by 821
A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
Lerner, B., & Lawrence, N. D. (2001) · Neural Computing and Applications · doi:10.1007/s005210170016 · Cited by 29
A Comparative Study of Different Machine Learning Approaches for Decision Making
Kelemen, A., Liang, Y., & Franklin, S. (2005) · Cited by 23
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Ali, A. B. M. S., & Abraham, A. (2002) · HIS eBooks · Cited by 18
SVM learning from large training data set
Murphey, Y. L., Chen, Z., Putrus, M., & Feldkamp, L. A. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1224025 · Cited by 8
Self-citation
Flagged first, then by citations
1 of the 8 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
State of the Art in Defect Detection Based on Machine Vision
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00343-6 · Cited by 896
A Generic Deep-Learning-Based Approach for Automated Surface Inspection
Ren, R., Hung, T., & Tan, K. C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2668395 · Cited by 485
Image-Based Surface Defect Detection Using Deep Learning: A Review
Bhatt, P. M., Malhan, R. K., Rajendran, P. S., Shah, B. C., Thakar, S., Yoon, Y. J., & Gupta, S. K. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4049535 · Cited by 392
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
“In [2], defect detection was modelled as a nonlinear classification problem using a SVM with RBF as the kernel function.”
BackgroundMethodInfluentialRecent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287
Metadata
- Authors
- Jia, H., Murphey, Y. L., Shi, J., & Chang, T.-S.
- Affiliations
- University of Michigan (US)
- OG Technologies (United States) (US)
- Venue
- Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
- Publisher
- IEEE
- Published
- 2004
- Pages
- 239-242 Vol.3
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W4250878893
- Cited by
- 174
- References
- 8
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 16 · 2022: 17 · 2021: 13 · 2020: 3 · 2019: 14 · 2018: 12 · 2017: 12 · 2016: 7 · 2015: 6 · 2014: 5 · 2013: 8 · 2012: 7
- FWCI
- 2.99
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (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, rolled steel, real-time visual inspection, support vector machine, feature extraction, steel quality control, image processing
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