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Because anomalous samples cannot be used for training, many anomaly detectionand localization methods use pre-trained networks and non-parametric modelingto estimate encoded feature distribution. However, these methods neglect theimpact of position and neighborhood information on the distribution of normalfeatures. To overcome this, we propose a new algorithm, \textbf{PNI}, whichestimates the normal distribution using conditional probability givenneighborhood features, modeled with a multi-layer perceptron network. Moreover,position information is utilized by creating a histogram of representativefeatures at each position. Instead of simply resizing the anomaly map, theproposed method employs an additional refine network trained on syntheticanomaly images to better interpolate and account for the shape and edge of theinput image. We conducted experiments on the MVTec AD benchmark dataset andachieved state-of-the-art performance, with \textbf{99.56\%} and\textbf{98.98\%} AUROC scores in anomaly detection and localization,respectively.

PNI : Industrial Anomaly Detection using Position and Neighborhood Information