Continual Learning with Out-of-Distribution Data Detection for Defect Classification

We propose a framework for defect detection in production lines that leverages deep learning models, out-of-distribution (OOD) detection, and continual learning to address the challenges of unknown defects and catastrophic forgetting. The proposed method divides classifier training into chronicle tasks, each introducing new defect classes and leveraging OOD detection to classify unknown defects. We evaluate the framework on a highly unbalanced product defect dataset and demonstrated that it outperformed existing approaches, improving the average F-score by 10%. Our method also improve the performance of the PODNet and DER models, but not the WA model due to its poor performance on our dataset. These results suggest that the proposed method has the potential to improve defect detection in production lines, especially for small-quantity-wide-variety production scenarios.