DTSD: A Dual Teacher-Student-Based Discrimination Model for Anomaly Detection

The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this article, we propose a dual teacher–student-based discrimination (DTSD) model for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher‒student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder, and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher‒student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher–student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available at https://github.com/Math-Computer/DTSD .