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

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arxiv 2203.03429 v1 pith:HUCCM2FK submitted 2022-03-03 cs.LG

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

classification cs.LG
keywords datadefectdisplaygenerationinspectionqualitysyntheticfront-of-screen
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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