[3D1-GS-2] Machine learning: Image recognition

[3D1-GS-2-02] Sample selection method for noisy training data in anomaly detection with a focus on industrial inspection

〇Yasufumi Kunisada¹, Akimitsu Kawano¹, Yuichi Furukawa¹, Teruaki Saitou¹ (1. Oki Electric Industry Co., Ltd.)

Keywords:Anomaly Detection, Industrial Inspection

In the manufacturing process of industrial products such as electronic substrates, visual inspections are conducted to remove product defects. In recent visual inspection systems, anomaly detection using machine learning is known as a highly accurate method . While these methods are evaluated under ideal experimental settings with clean training data, the inclusion of noisy data can degrade accuracy under practical settings. In this paper, we propose the sample selection method for noisy training data in anomaly detection. We improve convenience of the conventional method, SoftPatch. SoftPatch does not identify the noisy data at the data-level because it performs selection at the image patch-level. It also cannot be applied to other anomaly detection methods. On the other hand, our proposed method identify the noisy data and applicable to other methods. Experimental results using industrial product data demonstrate that our proposed method maintains the accuracy as the conventional method while improving convenience.