research area Computer Visionconference SID
content type paperpublished March 2022

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

AuthorsShancong Mou, Meng Cao, Zhendong Hong, Ping Huang, Jiulong Shan, Jianjun Shi

View publication

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 defective 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.

Related readings and updates.

Synth4Seg – Learning Defect Data Synthesis for Defect Segmentation Using Bi-Level Optimization

June 22, 2025research area Computer Vision, research area Methods and Algorithmsconference IEEE Transactions on Automation Science and Engineering

Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. However, many current methods simply generate defects following a fixed set of rules, which may not directly relate to downstream task performance. This can lead to suboptimal performance and may even…
Read more

PAEDID: Patch Autoencoder-based Deep Image Decomposition for Unsupervised Anomaly Detection

March 13, 2023research area Computer Vision, research area Methods and Algorithmsconference IISE Transactions

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective region localization but lack accuracy in defective region shape contour…
Read more