Defect Detection Using Deep Lifelong Learning

With the rapid development of deep learning, automatic defect detection has been introduced into various manufacturing pipelines. Many studies on defect inspection focus on training an accurate model that can perform well on a certain defect type. However, as the manufacturing process evolves, new defect types may appear in practice. The model trained on old defect types will struggle to detect the new ones. To address this issue, we propose to use continual lifelong learning for defect detection. The deep model can increasingly learn to detect new defects yet keeping the learned ones non-forgetting without retraining on the previous data. Our approach can build a compact model, which increasingly learns to detect new defect types. Experimental results show that our approach can learn to detect new defect types incrementally while maintaining its original capability to detect the old defect types.