Surface Anomaly Detection and Localization with Diffusion-based Reconstruction

Surface anomaly detection aims to detect and locate anomalies in the product surface images. Anomalies are mostly rare and difficult to find, so the unsupervised method which trained using anomaly-free training samples is popular. One of the most successful unsupervised approaches is reconstruction-based method. This kind of method assumes that anomalies are much harder to be accurately reconstructed than normal samples, so the anomalies are detected by the differences between input images and reconstructed images. On the one hand, the detection performance is significantly determined by the quality of reconstruction, the reconstruction ability of current models needs to be improved. On the other hand, anomalies sometimes can also be well reconstructed, leading to detection failure. To address those challenges, we propose a novel diffusion-based image reconstruction method for anomaly detection (DRAD). Leveraging the capabilities of diffusion model to generate high-quality and diverse images, DRAD achieves better reconstruction results compared to previous methods. Additionally, we propose a noise embedding process during the reconstruction, which avoids the direct copying of anomalies. Extensive experiments demonstrate that the proposed DRAD method achieves state-of-the-art performance on the MVTec AD dataset, particularly in anomaly detection with 99.1% image-level AUROC.