Improved Anomaly Detection Based on Image Reconstruction and Global Template Features for Industrial Products

Anomaly detection in industry applications is a challenging problem when negative (defective) samples are unavailable, especially in the case where there are missing parts or foreign objects occupied a relatively large region. Conventional reconstruction-based approaches cannot guarantee the restored image being a normal one, leading to poor segmentation results. In this work, we propose an unsupervised anomaly detection approach to tackle the problem of large-area anomaly detection by incorporating global template features into an Auto-Encoder like reconstruction model. In particular, our model infers the value of each pixel based on both the surrounding local-neighborhood information and the global information encoded at the same pixel position. During the reconstruction phase, any abnormal features are then replaced with normal ones, avoiding over-reconstruction of large-area abnormalities. The experimental results in comparison with other methods demonstrate its effectiveness for industrial anomaly detection.