An Algorithm For Detecting Seam Cracks In Steel Plates
Open accessAbstract
In this study, we developed an algorithm for detecting seam cracks in a steel plate. Seam cracks are generated in the edge region of a steel plate. We used the Gabor filter and an adaptive double threshold method to detect them. To reduce the number of pseudo defects, features based on the shape of seam cracks were used. To evaluate the performance of the proposed algorithm, we tested 989 images with seam cracks and 9470 defect-free images. Experimental results show that the proposed algorithm is suitable for detecting seam cracks. However, it should be improved to increase the true positive rate.
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In [65], SVM with 12 geometric and gray-level features detected one type of defect in plate with 85% accuracy.
“SVM was used with 12 geometric and gray-level features in [65] and seven histogram and gradient-based features in [67] as inputs to detect one type of defect in plate and slab with 85% and 94% accuracy, respectively.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [65]
The Introduction (p. 2835) identifies seam cracks in steel plates as the detection target, and the Feature Extraction section (p. 2836) lists shape-related features and gray-value difference. The passages available from our copy do not establish that the method used SVM, had 12 input features, or achieved 85% accuracy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSVM 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 [65]
The Method section describes using extracted features to distinguish seam cracks from pseudo defects in steel plates. It does not identify SVM as the method or establish that SVM is widely used for this purpose (searched: SVM, support vector machine, classifier). (LLM, conf=0.84)
Experimental — accuracy not yet measured
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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
- Authors
- Choi, D.-C., Jeon, Y.-J., Yun, J. P., Yun, S. W., & Kim, S. W.
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2012-12-20
- Volume
- 6
- Issue
- 12
- Pages
- 2835-2838
- OpenAlex
- W1569967531
- Cited by
- 6
- References
- 0
- Citations by year
- 2021: 1 · 2020: 1 · 2016: 1 · 2014: 3
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering
- 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
- steel plate, Gabor filters, non-destructive testing
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