Inpainting-based Anomaly Detection System with Self-Supervised Learning

Deep learning for defect detection has become a critical imperative in contemporary electronics manufacturing. We propose an inpainting-based anomaly detection system to identify defects without labeled defects. An image inpainting model, which discerns disparities between the original and restored versions of the defective image, is designed as the core of our methodology. To further address issues related to reconstructing asymmetric images with defects, we incorporate self-supervised learning (SSL) to extract a broader spectrum of features. In experiments, we compare the proposed method to state-of-the-art models based on MVTec open dataset. Our proposed method can achieve a best performance of 97%, and surpasses the SOTA model by a margin of 57%.