Automated Anomaly Detection for Surface Defects by Dual Generative Networks With Limited Training Data

The manufacturing process in the pharmaceutical industry requires for using vessels or tanks which inner surfaces can undergo fouling and/or may develop visible defects that have to be cleaned away. Surface contamination can present itself in different forms, and there is no available image database of stains. In this paper, we propose to use an unsupervised anomaly detection approach which trains models on one or just a few images of clean surfaces. First, we propose an inpainting GAN with global and local generators to make full use of every single image and to reduce the overfitting caused by a limited number of images in the training dataset. Furthermore, we propose a periodic noise injection technique to increase the number of images for training and to improve the detection performance of the network. The experimental results demonstrate that the proposed network and noise injection technique achieve significant performance gains in the challenging task of anomaly detection on the surfaces of pharmaceutical equipment and other real-world industrial datasets. We also consider possible generalizations of the proposed method.