Papers › PNI : Industrial Anomaly Detection using Position and Neighborhood Information
PNI : Industrial Anomaly Detection using Position and Neighborhood Information
Jaehyeok Bae, Jae-Han Lee, Seyun Kim
Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution. However, these methods neglect the impact of position and neighborhood information on the distribution of normal features. To overcome this, we propose a new algorithm, \textbf{PNI}, which estimates the normal distribution using conditional probability given neighborhood features, modeled with a multi-layer perceptron network. Moreover, position information is utilized by creating a histogram of representative features at each position. Instead of simply resizing the anomaly map, the proposed method employs an additional refine network trained on synthetic anomaly images to better interpolate and account for the shape and edge of the input image. We conducted experiments on the MVTec AD benchmark dataset and achieved state-of-the-art performance, with \textbf{99.56\%} and \textbf{98.98\%} AUROC scores in anomaly detection and localization, respectively.
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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | BTAD | PNI | Segmentation AUROC | 97.8 | #13 of 15 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI Ensemble | Detection AUROC | 99.63 | #16 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI Ensemble | Segmentation AUPRO | 96.55 | #16 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI Ensemble | Segmentation AUROC | 99.06 | #16 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI | Detection AUROC | 99.56 | #24 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI | Segmentation AUPRO | 96.05 | #24 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | PNI | Segmentation AUROC | 98.98 | #24 of 148 | Archive leaderboard | report |
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Methods
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