Position-Aware PatchCore: Anomaly Detection by Nearest Neighbor of Pre-Trained Deep Features with Positional Information

In recent years, unsupervised anomaly detection methods based on the intermediate features of pretrained Deep Neural Networks have demonstrated high accuracy on benchmark datasets. In particular, PatchCore is expected to be applied in real-world visual inspection not only for accuracy but also for memory efficiency. However, PatchCore has a limitation in precisely detecting locationally-constrained anomalies such as incorrect placement or missing parts. In this study, Position-Aware PatchCore is proposed, a new anomaly detection method that incorporates position information, feature variance information and neighborhood normal feature information into PatchCore. Experiments using MVTec AD show that Position-Aware PatchCore achieves the highest pixel-level detection accuracy for locationally-constrained anomalies among the comparison methods while maintaining memory consumption comparable to PatchCore.