Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network

Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. Although nowadays image reconstruction-based methods are widely being used in various anomaly detection applications, they cannot effectively learn semantic representation, which leads to imperfect reconstruction. In this article, anomaly detection is formulated as a joint problem of feature reconstruction and inpainting in the dual-siamese framework. The proposed approach forces the network to model the feature distribution from the normal area and capture the semantic context for discriminating normal and abnormal areas. It first uses a Siamese architecture to capture discriminative features of defect-free samples and its corresponding defective samples generated by the defect random generation module. A dense feature fusion module is then employed to obtain the dense feature representation of dual input. The second Siamese network is proposed to reconstruct and inpaint the dual-dense features of the previous stage. Compared to the existing methods that mostly employ single image reconstruction, it is beneficial to simultaneously reconstruct and inpaint the information of dense discriminative features. The experimental results on the MVTec AD datasets and some major real industrial datasets demonstrate that our method achieves state-of-the-art inspection accuracy.