Bwcon (Germany)

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.