Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques

TL;DR

This paper demonstrates that combining anomaly maps with supervised classification models significantly improves defect detection accuracy in manufacturing, maintaining robustness across different classification settings and datasets.

Contribution

It introduces a novel approach of using anomaly maps as additional input to supervised models, enhancing defect detection performance in industrial applications.

Findings

Best performance achieved with image and anomaly map input

Method is robust to class balancing policies

Effective across multiple real-world datasets

Abstract

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps 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 provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.