Enhanced Structural Anomaly Detection through Improved Image Inpainting and Feature-Level Discrimination

In industrial production, the detection of structural anomalies on product surfaces is crucial for quality control. Traditional encoder-decoder architectures for unsupervised anomaly detection, while effective, tend to reduce the reconstruction error between normal and abnormal samples due to their generalization capabilities. To address this, we propose an enhanced structural anomaly detection algorithm based on improved image inpainting. By converting the reconstruction task into an inpainting-filling-reconstruction process, we amplify the reconstruction error between normal and abnormal samples. Our algorithm uses a feature loss and feature-level anomaly discrimination method to mitigate noise interference. Furthermore, we introduce a lightweight U-Net design to meet industrial requirements. Experimental results on the MVTec LOCO AD dataset demonstrate that our approach outperforms existing algorithms, achieving an average AUROC of 85.0% for anomaly detection and 88.5% for anomaly localization, highlighting its potential in industrial applications.