A fast method of steel surface defect detection using decision trees applied to LBP based features
Abstract
Surface defect detection plays a significant role in quality enhancement in steel manufacturing. Support Vector Machines and neural networks are the most popular classifiers in this application. Decision trees are also known as other classifiers for steel defect detection yielding a fast but moderate performance. In this paper, we introduce a more accurate classification method by using decision trees and applying Principal Component Analysis (PCA) and Bootstrap Aggregating (Bagging) on features being extracted by a local binary pattern based operator. This methodology yields an enhanced accuracy and reinstates decision trees as fast and accurate classifiers for two-class classification of steel surface defects. In order to have a complete classification in a real-time automatic surface inspection, a multiclass Support Vector Machine should be cascaded to the decision tree classifier. The proposed classification system is considerably faster than the traditional schemes.
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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 [82]
Local binary pattern is sensitive to scale variation and noise interference.
“Sensitive to scale variation and noise interference.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [82]
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- Authors
- Aghdam, S. R., Amid, E., & Imani, M. F.
- Affiliations
- Amirkabir University of Technology (IR)
- Venue
- 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
- Publisher
- IEEE
- Published
- 2012-07
- Pages
- 1447-1452
- OpenAlex
- W1991589289
- Cited by
- 46
- References
- 11
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 7 · 2023: 4 · 2022: 8 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 2 · 2017: 1 · 2016: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Industrial Electronics and Applications (ICIEA) (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 › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- steel surface defect detection, decision tree, local binary pattern, feature extraction, principal component analysis, bagging, binary classification, multi-class support vector machine
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