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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For long products, particularly wire rods, speed could be as high as 225 miles/h (100 m/s).
“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]
First-order gradient filter followed by thresholding is more commonly used for edge localisation due to its noise immunity characteristics compared to Laplacian.
“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]
Region growing has been employed, particularly after use of gradient operators, to identify RoIs.
“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]
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 [2]
In [2], use of horizontal gradient operator for detection of vertical edge of seam defects also simultaneously detected equal number of pseudo defects.
“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]
Based on seam defect characteristics being very thin and darker than background, seed points were identified and subsequent region growing step provided RoIs.
“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]
SVM has been widely used for separation of defects from pseudo defects in steel surfaces.
“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]
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 [2]
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.
“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]
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Real-Time Steel Inspection System Based on Support Vector Machine and Multiple Kernel Learning
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- 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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