Fanuc Corporation

Yujiao Cheng

Tetsuaki Kato

A method and system 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. The feature data includes hundreds of feature vectors, each having hundreds of filter dimensions. A weight value of one is initially assigned to each filter, and test data comprising some good and some bad parts is evaluated by a weighted k-NN module to determine an anomaly score from the weighted feature data. After all test images are evaluated, good and bad data points nearest a threshold are selected and a gradient ascent computation is performed to update the filter weights. Anomaly scoring and gradient ascent are performed iteratively until filter weights are identified which maximize the separation between good and bad scores, thereby eliminating missed detections and false anomalies.

Feature weighting for kNN-based anomaly detection

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.

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Fremont, CA, United States of America

Yujiao Cheng