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arXiv · 2203.03429

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

Shancong Mou·Meng Cao·Zhendong Hong·Ping Huang·Jiulong Shan·Jianjun Shi
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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.

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Shancong Mou, Meng Cao, Zhendong Hong, Ping Huang, Jiulong Shan, Jianjun Shi. 2022-03-03. Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey. https://arxiv.org/abs/2203.03429

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