Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection

Understand

Reconstruction-based methods are widely explored in industrial visual anomaly detection.

• Such methods commonly require the model to well reconstruct the normal patterns but fail in the anomalies, and thus the anomalies can be detected by evaluating the reconstruction errors.
• However, in practice, it's usually difficult to control the generalization boundary of the model.
• The model with an overly strong generalization capability can even well reconstruct the abnormal regions, making them less distinguishable, while the model with a poor generalization capability can not reconstruct those changeable high-frequency components in the normal regions, which ultimately leads to false positives.