Self-Attention Enhanced Student–Teacher Framework for Visual Anomaly Detection in Industrial Images

In this work we introduce AeCSAD, a student–teacher framework designed to detect two types of anomalies in images of industrial components: structural anomalies (physical defects) and logical anomalies (incorrect relationships between components). Unlike prior methods, AeCSAD extends the component segmentation–based logical anomaly detection scheme (c. 2024) with self-attention mechanisms, enabling more effective relational modeling. We demonstrate consistent improvements on the MVTec LOCO Anomaly Detection benchmark. Specifically, AeCSAD employs a global student network with self-attention for reasoning across distant components, complemented by a local student network for fine-grained analysis. Additionally, a patch histogram module measures the frequency distribution of components, allowing the system to detect irregularities in their occurrence. During inference, anomaly scores from the histogram module and the fused local–global networks are combined to produce the final anomaly score. Experiments show that AeCSAD achieves superior average AUROC performance on both structural and logical anomaly detection tasks compared to prior approaches.