Threshold and model adaptation for the knn-based anomaly detection method

Abstract

A method for anomaly detection from object images. A feature extractor provides feature data characterizing part images, including good parts and bad parts. Training data for good parts is used to create a k-nearest neighbors (k-NN) model core set. Adaptation data for some good and some bad parts is evaluated by a k-NN module to determine an anomaly score from the feature data. A good-vs-bad threshold for the anomaly score is initially set to a low value. When an anomaly score over the threshold is detected, a human inspects the corresponding image to determine whether the subject part is good or bad. If the part is good and the anomaly was false, the k-NN model core set is updated to include the feature data for the subject part, and the threshold is adjusted higher. After every adaptation, the k-NN model is applied to a test data set to evaluate performance.