Feature Bank-Guided Reconstruction for Anomaly Detection

Abstract

Visual surface anomaly detection targets the location of anomalies, with numerous methods available to address the challenge. Reconstruction-based methods are popular for their adaptability and interpretability. However, reconstruction-based methods currently struggle with the challenge of achieving low image fidelity and a tendency to reconstruct anomalies. To overcome these challenges, we introduces the Feature Bank-guided Reconstruction method (FBR), incorporating three innovative modules: anomaly simulation, feature bank module, and a cross-fused Discrete Cosine Transform channel attention module. Guided by these modules, our method is capable of reconstructing images with enhanced robustness. The experimental results validate the effectiveness of the proposed approach, which not only achieves outstanding performance on the BeanTech AD dataset with an 96.4% image-AUROC and a 97.3% pixel-AUROC, but also demonstrates competitive performance on the MVTec AD dataset with a 99.5% image-AUROC and a 98.3% pixel-AUROC.