Normalizing Flow‐Based Industrial Complex Background Anomaly Detection

Abstract

This paper proposes a novel approach called cross‐scale with attention normalizing flow (CSA‐Flow) enhanced with channel‐attention (CA) and self‐attention (SA) modules for high‐speed railway anomaly detection in complex industrial backgrounds to reduce the manual workload of the primary maintenance of high‐speed electric multiple units. Detecting defects in industrial environments, characterized by intricate backgrounds and unclear subjects, poses significant challenges. To address this, CSA‐Flow introduces a channel feature extraction module that combining the pretrained convolutional neural network models with a CA module for feature extraction, capturing information at different scales, and uses the SA module to capture more contextual information by its larger receptive field. The performance evaluation of CSA‐Flow on the MVTec‐AD dataset demonstrates an impressive area under the receiver operating characteristic curve (AUROC) score of 98.7%, with an equally remarkable score of 98.4% across all object classes. To further assess the effectiveness of CSA‐Flow in complex background scenarios, we introduce a dedicated dataset, specifically designed for high‐speed rail braking devices (HSRBDs). The experimental results establish the superiority of CSA‐Flow over current state‐of‐the‐art approaches in terms of both AUROC score and recall score, validating its exceptional capability for detecting anomalies in industrial complex backgrounds.