Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques

Abstract

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The Discriminatively trained Reconstruction Anomaly Embedding Model (DRAEM) technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps (images highlighting areas where defects probably lie) can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting offers consistent performance when framing defect detection as a binary or multiclass classification problem and is unaffected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.