Unsupervised Autoencoder Approach for Precise Line-Type Mura Detection and Classification - Chang, Ting-Yu; Lin, Chia-Yu (IEEE International Conference on Consumer Electronics-China (Online), 2025)
Mura refers to surface defects or uneven brightness in panel manufacturing and is classified by severity into light Mura and serious Mura. Due to limited data, traditional object detection is not feasible. Instead, we propose an unsupervised method to classify serious Mura and accurately localize defects. We combine an autoencoder with computer vision to simulate a supervised model. This approach not only improves defect reconstruction quality but also achieves 90% precision while improving recall by 30%. Our method enhances defect detection accuracy, providing a data-efficient, scalable solution for quality control in panel manufacturing