Papers › N-pad : Neighboring Pixel-based Industrial Anomaly Detection
N-pad : Neighboring Pixel-based Industrial Anomaly Detection
JunKyu Jang, Eugene Hwang, Sung-Hyuk Park
Identifying defects in the images of industrial products has been an important task to enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly detection models were developed using pre-trained networks to learn nominal representations. To employ the relative positional information of each pixel, we present \textit{\textbf{N-pad}}, a novel method for anomaly detection and segmentation in a one-class learning setting that includes the neighborhood of the target pixel for model training and evaluation. Within the model architecture, pixel-wise nominal distributions are estimated by using the features of neighboring pixels with the target pixel to allow possible marginal misalignment. Moreover, the centroids from clusters of nominal features are identified as a representative nominal set. Accordingly, anomaly scores are inferred based on the Mahalanobis distances and Euclidean distances between the target pixel and the estimated distributions or the centroid set, respectively. Thus, we have achieved state-of-the-art performance in MVTec-AD with AUROC of 99.37 for anomaly detection and 98.75 for anomaly segmentation, reducing the error by 34% compared to the next best performing model. Experiments in various settings further validate our model.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | N-pad | Detection AUROC | 99.37 | #38 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | N-pad | Segmentation AUPRO | 95.1 | #38 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | N-pad | Segmentation AUROC | 98.75 | #38 of 148 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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