Anomaly-prior guided inpainting for industrial visual anomaly detection

Abstract

Visual anomaly detection aims to identify areas where the appearance deviates from the normal distribution. Reconstruction-based methods detect anomalies through the analysis of reconstruction errors. In particular, Inpainting-based methods aim to reduce the potential for network generalization of anomalies by introducing random masks. However, larger anomalies may still be reconstructed, making them challenging to discriminate. Although enhancing the network’s long-distance modeling capability offers the possibility to mitigate the reconstruction of anomalous regions, there is a possibility that the reconstruction error for normal regions increases and the ability to discriminate small anomalies is reduced due to the random nature of the mask placement. To address this issue, we introduce a correction branch to modify the original reconstruction results obtained from the reconstruction network during testing. This method can alleviate the potential increase in reconstruction errors in normal regions caused by the randomness of the mask to some extent and enhance the network’s responsiveness to anomalous regions. We also propose a reconstruction network constructed with ConvNeXt blocks and the incorporation of the channel attention mechanism, which enhances the long-distance modeling ability, thereby improving the model’s predictive capacity for large masked areas, this network also improves the reconstruction of textures, thereby improving the network’s capacity to discriminate anomalies. The proposed method achieves competitive results on the challenging benchmark MVTec AD and BTAD datasets.