English

Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey

Machine Learning 2022-03-08 v1

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 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.

Keywords

Cite

@article{arxiv.2203.03429,
  title  = {Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey},
  author = {Shancong Mou and Meng Cao and Zhendong Hong and Ping Huang and Jiulong Shan and Jianjun Shi},
  journal= {arXiv preprint arXiv:2203.03429},
  year   = {2022}
}