カテゴリーに特化したハンドクラフト特徴量による異常検知モデル

抄録

In this paper, a highly accurate anomaly detection method using handcrafted feature extraction is presented for particular categories of MVTec AD, which are benchmark data sets of anomaly detection. In this method, local features based on grey level gradients are sampled in a subset by greedy method, and anomaly is detected by Euclidean distance. Evaluations for the category "screw" showed that the proposed method gained higher AUROC than PatchCore, which is one of State-of-the-art of deep-learning model.