Vertical scratch detection algorithm for high-speed scale-covered steel BIC(Bar in Coil)
Abstract
Recently, vision-based inspection systems have been widely investigated for the defect inspection in various industrial products. This paper proposes a vision-based method for detection of scratches in the surfaces of scale-covered steel wire rods. Scales are formed on the surface of wire rods owing to the deposition of oxidized substances that are produces during manufacturing. Because of the variety in the types of steel, presence of scales, and manufacturing conditions, the features of wire rod surface images are not uniform. Moreover, the similarities in the gray-level and shape of the defect and defect-free regions make it very difficult to accurately detect defects. In order to solve the abovementioned problems and to detect defects more effectively, we propose a new defect detection algorithm, which is based on edge pair detection, binarization with double threshold, morphological operation, and SVM (Support Vector Machine). Finally, the experimental results conducted on steel wire rod images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel wire rods.
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At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation.
“At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation [5,44,58,59].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [44]
The problem of uneven intensity in rod/bar is reduced to some extent by using more cameras than strictly required.
“This problem is reduced to some extent by using more number of cameras than strictly required as in [5,44] (five cameras) and [85] (four cameras).”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [44]
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 [44]
Yun et al. used first-order horizontal gradient filter followed by edge pair search, then morphological dilation and erosion to join edge pair, and rejected defect size less than a value to discard spurious defects for detecting vertical scratch in rods.
“Yun et al. [44]: To detect vertical scratch in rods, firstorder horizontal gradient filter was first used followed by an edge pair search. Then, morphological dilation and erosion was used to join edge pair. Thereafter, defect size (length) less than a value was rejected to discard spurious defects.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [44]
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 [44]
Cites
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
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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
“At the beginning of image processing, bar image is recovered from the background for better outcome of defect segmentation [5,44,58,59].”
BackgroundMethodInfluentialResearch 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
Wang, Y., Xia, H., Yuan, X., Li, L., & Sun, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5238-0 · Cited by 61 · see citing works
- Authors
- Yun, J. P., Park, C., Bae, H., Hwang, H., & Choi, S.
- Affiliations
- Pohang Iron and Steel (South Korea) (KR)
- Venue
- ICCAS 2010
- Publisher
- IEEE
- Published
- 2010-10
- Pages
- 342-345
- OpenAlex
- W1785458974
- Cited by
- 10
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 1 · 2021: 2 · 2018: 2 · 2017: 2 · 2014: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Control, Automation and Systems (ICCAS) (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
- scratch detection, surface defect detection, image-based inspection, morphological operations, support vector machine, high-speed inspection
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