Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification
- Submitting institution
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University of Hertfordshire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 16267272
- Type
- D - Journal article
- DOI
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10.1109/TIM.2018.2852918
- Title of journal
- IEEE Transactions on Instrumentation and Measurement
- Article number
- -
- First page
- 667
- Volume
- 68
- Issue
- 3
- ISSN
- 0018-9456
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- International collaboration between UH and the Central South University, China, Hefei University of Technology, China and Wuhan University, China. The research was funded by the National Natural Science Foundation of China under Grant 51704089, Grant 51577046, and Grant 51637004, the China Postdoctoral Science Foundation under Grant 2017M621996 and by the Fundamental Research Funds of the Central Universities of China under Grant JZ2018YYPY0296. Comprehensive experiments using real-world hot-rolled strips data were conducted. The defect classification provided is of benefit to online quality control and will increase production in architecture and in machinery, steel and automobile industries.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -