Abstract
Inspecting defects in LCD manufacturing is of uttermost importance to ensure customer’s satisfaction and reduce time and money losses. Deep learning classification methods rely on closed-set assumption that the classes to predict during operation are the same as the training ones. However, in real-world settings, new unseen classes (defects) often arise. In this work we evaluate the capabilities of state-of-the-art deep learning methods of classifying known and unknown defects on LCD images. Given the limited performance of such methods, we here propose a novel Cluster Error (CE) classifier and a strong-repulsive (SR) training loss for feature clustering to enhance the classification accuracy both on known and unknown defects. Our results on two real-world industrial datasets show the challenges of such task and how our classifier outperforms the other methods.
Citation
F. Cursi, M. Wittstamm, W. L. Sung, A. Roy, C. Zhang and B. Drescher, "Feature Clustering for Open-Set Recognition in LCD Manufacturing," in IEEE Transactions on Instrumentation and Measurement, https://doi.org/10.1109/TIM.2023.3308248.