Implicit Foreground-Guided Network for Anomaly Detection and Localization

Abstract

Anomaly detection plays an essential role in large-scale industrial manufacturing. However, reconstruction-based anomaly detection methods, as one of the mainstream methods, are prone to incorrectly detecting background noise as anomalous regions. Therefore, inspired by multi-task learning, we propose an Implicit Foreground-guided Network (IFgNet), which consists of a Multi-Task Attention Shared (MTAS) sub-network and a discriminative sub-network. Specifically, the MTAS sub-network implements the foreground detection and reconstruction tasks within the shared network, while the discriminative sub-network performs the final anomaly detection. In the MTAS sub-network, multiple task-specific attention blocks are applied to learn task-specific features while allowing features to be shared between different tasks. Consequently, the features that contain both semantic and edge structure information are learned through the foreground detection task, which also facilitates the reconstruction task. Furthermore, the outputs of foreground detection can be utilized to refine the anomaly detection results. In this way, IFgNet effectively mitigates the influence of background noise and achieves competitive performance on the VisA and BTAD datasets with existing methods.