Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
This is a survey paper, so it is incremental, summarizing existing methods without new contributions.
The paper reviews synthetic data generation methods and evaluation metrics to address the severe data imbalance, particularly the limited defect samples, in display front-of-screen quality inspection for manufacturing.
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.