论文标题
显示屏幕前质量检查的合成缺陷生成：调查
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
论文作者
论文摘要
显示屏幕前（FOS）质量检查对于制造过程中显示器的大规模生产至关重要。但是，严重的不平衡数据，尤其是缺陷样本数量有限，一直是一个长期存在的问题，它阻碍了深度学习算法的成功应用。合成缺陷数据生成可以帮助解决此问题。本文回顾了最先进的合成数据生成方法和可能应用于显示FOS质量检查任务的评估指标。
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 defect 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.
