Application of Undecimated Wavelet Transform to Surface Defect Detection of Hot Rolled Steel Plates
Abstract
Characteristics of surface defects of hot rolled steel plates were analyzed, and an algorithm for detection of hot rolled steel plates was developed. Based on undecimated wavelet transform and mathematical morphology, the algorithm can solve the problem of false alarms by scales and water marks. Positions of defects were determined by modular maximum of inter-scale correlation of wavelet coefficient at first, and then the defects were classified with priori knowledge about characteristic of surface defects on hot rolled steel plates. Experimental results shows that the algorithm is not sensitive to disturbance of scales, water marks and uneven illumination, and the recognition rate of defects is up to 90.23%.
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Wavelet smoothing can sharpen an object's edges and restrain noise; moreover, it can enhance the positioning precision of the edges and the depiction of the images.
“Wavelet smoothing can sharpen an object's edges and restrain noise; moreover, it can enhance the positioning precision of the edges and the depiction of the images.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [61]
Wavelet transform has difficulty choosing a wavelet basis.
“Difficulty in choosing wavelet basis.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [61]
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Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
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“In a large number of instances [3,35,41,42,56,58,61], it has been possible to localise RoIs with a high degree of accuracy.”
BackgroundMethodInfluentialAutomatic Defect Detection on Hot-Rolled Flat Steel Products
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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
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An evolutionary classifier for steel surface defects with small sample set
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- Authors
- Wu, X.-Y., Xu, K., & Xu, J.-W.
- Affiliations
- University of Science and Technology Beijing (CN)
- ORCID
- Xu, Ke
- Venue
- 2008 Congress on Image and Signal Processing
- Publisher
- IEEE
- Published
- 2008
- Pages
- 528-532
- OpenAlex
- W2064014153
- Cited by
- 53
- References
- 8
- Citations by year
- 2025: 2 · 2024: 8 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 2 · 2017: 3 · 2015: 1 · 2014: 1 · 2013: 2 · 2012: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Congress on Image and Signal Processing (CISP) (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Key Technologies Research and Development Program
- 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
- stationary wavelet transform, surface defect detection, hot-rolled steel plate, mathematical morphology, false alarm reduction, inter-scale correlation, defect classification, non-uniform illumination
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