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
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.
PAEDID: Patch Autoencoder-based Deep Image Decomposition for Unsupervised Anomaly Detection
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…
See paper detailsUnderstanding Screen Relationships from Screenshots of Smartphone Applications
All graphical user interfaces are comprised of one or more screens that may be shown to the user depending on their interactions. Identifying different screens of an app and understanding the type of changes that happen on the screens is a challenging task that can be applied in many areas including automatic app crawling, playback of app automation macros and large scale app dataset analysis. For example, an automated app crawler needs to…
See paper details
Discover opportunities in Machine Learning.
Our research in machine learning breaks new ground every day.