earticle

영어

The challenge of defect detection in Liquid Crystal Display (LCD) manufacturing is significant. This study proposes a data augmentation technique utilizing Generative Adversarial Networks (GAN) to improve defect identification accuracy. By generating synthetic image data with GAN, the original dataset is expanded, making it more diverse. This augmentation approach aims to improve the model's generalization capability and robustness with real-world data. Unlike traditional data augmentation, GAN-synthesized data provides more realistic and varied data. Experiments show that merging GAN-generated data with the original dataset improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This method suggests a viable data augmentation strategy for better quality control in LCD production.