MLDFR: A Multilevel Features Restoration Method Based on Damaged Images for Anomaly Detection and Localization

For unsupervised anomaly detection and localization, a common approach is learning the distribution of normal samples and then use it as a criterion to identify abnormalities. This article proposes a multilevel features restoration method based on damaged images (MLDFR) for anomaly detection and localization. MLDFR seeks to restore the "normal feature" of the test sample. Specifically, we damage the training samples to generate the corresponding samples, and then design a concurrent feature extractor utilizing convolutional neural network and transformer pretrained on ImageNet to completely represent the multilevel features of samples. Additionally, we fully consider the dependencies among local features over long distances and design a feature restoration module. On the challenging, widely used anomaly detection datase (MVTec-AD), metal parts defect detection dataset (MPDD), and beantech anomaly detection dataset (BTAD) of real-world datasets, MLDFR achieves state-of-the-art anomaly localization performance, as well as image-level detection with virtually flawless score. We further report ablation studies, demonstrating MLDFR's effectiveness and generalizability.