Abstract
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However,, the severe imbalanced data, especially the limited number of defective samples, has been a long-standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state-of-the-art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 975-978 |
| Number of pages | 4 |
| Journal | Digest of Technical Papers - SID International Symposium |
| Volume | 53 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2022 |
| Externally published | Yes |
| Event | 59th International Symposium, Seminar and Exhibition, Display Week 2022 - San Jose, United States Duration: May 8 2022 → May 13 2022 |
Bibliographical note
Publisher Copyright:© 2022 SID.
Keywords
- Display quality inspection
- Synthetic defect generation review
