Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection

TL;DR

This paper introduces MFRNet, a novel unsupervised industrial anomaly detection method that enhances image reconstruction by combining multi-scale feature aggregation with crossed-mask restoration, leading to improved detection accuracy.

Contribution

The paper proposes a multi-feature reconstruction network with crossed-mask restoration, improving anomaly detection by better feature representation and high-quality image repair.

Findings

Outperforms state-of-the-art methods on multiple datasets

Achieves high accuracy in industrial anomaly detection

Effectively reconstructs images with minimal abnormal regeneration

Abstract

Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing. Although existing reconstruction-based methods have achieved promising results, they still face two problems: poor distinguishable information in image reconstruction and well abnormal regeneration caused by model under-regularization. To overcome the above issues, we convert the image reconstruction into a combination of parallel feature restorations and propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. Specifically, a multi-scale feature aggregator is first developed to generate more discriminative hierarchical representations of the input images from a pre-trained model. Subsequently, a crossed-mask generator is adopted to randomly cover the extracted feature map, followed by a restoration network based on the transformer structure for high-quality repair of the missing regions. Finally, a hybrid loss is equipped to guide model training and anomaly estimation, which gives consideration to both the pixel and structural similarity. Extensive experiments show that our method is highly competitive with or significantly outperforms other state-of-the-arts on four public available datasets and one self-made dataset.