An Algorithm for Surface Defect Identification of Steel Plates Based on Genetic Algorithm and Extreme Learning Machine
Open accessAbstract
Defects on the surface of steel plates are one of the most important factors affecting the quality of steel plates. It is of great importance to detect such defects through online surface inspection systems, whose ability of defect identification comes from self-learning through training samples. Extreme Learning Machine (ELM) is a fast machine learning algorithm with a high accuracy of identification. ELM is implemented by a hidden matrix generated with random initialization parameters, while different parameters usually result in different performances. To solve this problem, an improved ELM algorithm combined with a Genetic Algorithm was proposed and applied for the surface defect identification of hot rolled steel plates. The output matrix of the ELM’s hidden layers was treated as a chromosome, and some novel iteration rules were added. The algorithm was tested with 1675 samples of hot rolled steel plates, including pockmarks, chaps, scars, longitudinal cracks, longitudinal scratches, scales, transverse cracks, transverse scratches, and roll marks. The results showed that the highest identification accuracies for the training and the testing set obtained by the G-ELM (Genetic Extreme Learning Machine) algorithm were 98.46% and 94.30%, respectively, which were about 5% higher than those obtained by the ELM algorithm.
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Local binary pattern is rotation and gray invariant and has fast calculation speed.
“Be of rotation and gray invariance, fast calculation speed.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [83]
Local binary pattern is sensitive to scale variation and noise interference.
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From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [83]
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Self-citation
- Authors
- Tian, S., & Xu, K.
- Affiliations
- University of Science and Technology Beijing (CN)
- Corresponding author
- Xu, Ke
- ORCID
- Xu, Ke
- Venue
- Metals
- Publisher
- MDPI AG
- Published
- 2017-08-15
- Volume
- 7
- Issue
- 8
- Pages
- 311
- ISSN
- 2075-4701 (ISSN-L)
- OpenAlex
- W2749372762
- Cited by
- 48
- References
- 19
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 3 · 2021: 11 · 2020: 9 · 2019: 6 · 2018: 4
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Metals (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- The National Natural Science Foundation of China (51674031)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- steel plate, hot-rolled steel, extreme learning machine, genetic algorithm, pockmarks, longitudinal cracks, defect classification
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