| 大会名称 |
|---|
| 2020年 情報科学技術フォーラム(FIT) |
| 大会コ－ド |
| F |
| 開催年 |
| 2020 |
| 発行日 |
| 2020-08-18 |
| セッション番号 |
| 2e |
| セッション名 |
| 情報論的学習理論と機械学習（１） |
| 講演日 |
| 2020/09/01 |
| 講演場所(会議室等) |
| e |
| 講演番号 |
| F-005 |
| タイトル |
| Glass Surface Defect Grading using Machine Learning Methods |
| 著者名 |
| Monikka Roslianna Busto, Takashi Obi, Hiroyuki Suzuki, Joong Sun Lee, Pei Jiang, |
| キーワード |
| grading, glass surface, anomaly detection, segmentation |
| 抄録 |
| Current studies on surface defect analysis involve detection of defects during manufacturing with little assessment of damage severity. In this study, we extend the defect analysis to grading - determining overall severity of damaged glass surfaces. We propose a framework for the computer vision tasks to perform grading and explore improvements to machine learning methods for defect detection. Furthermore, we aggregate information from detected defects and train a supervised machine learning classifier to assess severity. Experimental results demonstrate feasibility of defect grading using a minimal amount of labeled data. |
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