Abstract
In the steel manufacturing area, researches for defect inspection receive a big attention for quality control. This paper proposes an algorithm to detect a scratch defect on steel billets. This algorithm takes ROIs (Regions of Interest), and extracts 11 features which represent properties of defect on a ROI. SVM (Support Vector Machine) is used to classify defect and normal ROIs. The algorithm classifies a frame image of a Billet as a defect image if there is one or more defect ROIs. In the experiments, the proposed algorithm had reliable classifying accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 256-261 |
| Number of pages | 6 |
| Journal | Journal of Institute of Control, Robotics and Systems |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2013 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Automation
- Defect detection
- Image analysis
- Inspection system
- Steel
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