Deep Anomaly Detection for Automotive Components by Oversampling

Abstract

Training of deep neural networks (DNNs) requires large amounts of data. However, the automotive components that are the subject of this research have an extreme lack of defective product data due to rapid model changes and a low defective product rate during the manufacturing process. Additionally, the anomaly areas are negligible. Data augmentation (DA), which increases data by image transformations, is a method for solving data deficiency. Particularly, a deep convolutional generative adversarial network (DCGAN) is frequently employed in the medical industry. DA is shown to have an effect on not small anomalies but on images that are accounted by the classification target for a large percentage of the total image.