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

03/03/2022
by   Shancong Mou, et al.
0

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.

READ FULL TEXT

page 1

page 2

research
01/11/2021

Cognitive Visual Inspection Service for LCD Manufacturing Industry

With the rapid growth of display devices, quality inspection via machine...
research
02/25/2022

Improving generalization with synthetic training data for deep learning based quality inspection

Automating quality inspection with computer vision techniques is often a...
research
07/07/2019

Void region segmentation in ball grid array using u-net approach and synthetic data

The quality inspection of solder balls by detecting and measuring the vo...
research
09/23/2022

Smart Active Sampling to enhance Quality Assurance Efficiency

We propose a new sampling strategy, called smart active sapling, for qua...
research
04/07/2021

Synthetic training data generation for deep learning based quality inspection

Deep learning is now the gold standard in computer vision-based quality ...
research
04/04/2022

Synthetic Graph Generation to Benchmark Graph Learning

Graph learning algorithms have attained state-of-the-art performance on ...
research
07/02/2022

Benchmarks for Industrial Inspection Based on Structured Light

Robustness and accuracy are two critical metrics for industrial inspecti...

Please sign up or login with your details

Forgot password? Click here to reset