Multi-Layer Feature Restoration and Projection Model for Unsupervised Anomaly Detection

Abstract

The anomaly detection of products is a classical problem in the field of computer vision. Image reconstruction-based methods have shown promising results in the field of abnormality detection. Most of the existing methods use convolutional neural networks to build encoding–decoding structures to do image restoration. However, the limited receptive field of convolutional neural networks makes the information considered in the image restoration process limited, and the downsampling in the encoder causes information loss, which is not conducive to performing fine-grained restoration of images. To solve this problem, we propose a multi-layer feature restoration and projection model (MLFRP), which enables the restoration process to be carried out on multi-scale feature maps through a block-level feature restoration module that fully considers the detail information and semantic information required for the restoration process. We conducted in-depth experiments on the MvtecAD anomaly detection benchmark dataset, which showed that our model outperforms current state-of-the-art anomaly detection methods.