Anomaly detection from images in pipes using GAN

In recent years, the number of pipes that have exceeded their service life is increasing. Therefore, earthworm-type robots equipped with cameras have been developed to perform regularly inspections of sewage pipes. However, inspection methods have not yet been established. This paper proposes a method for anomaly detection from images in pipes using Generative Adversarial Network (GAN). A model that combines f-AnoGAN and Lightweight GAN is used to detect anomalies by taking the difference between input images and generated images. Since the GANs are only trained with non-defective images, they are able to convert an image containing defects into one without them. Subtraction images is used to estimate the location of anomalies. Experiments were conducted using actual images of cast iron pipes to confirm the effectiveness of the proposed method. Also validated using sewer-ml, a public dataset.