Generative Damage Learning for Concrete Aging Detection using Auto-flight Images

In order to health monitoring the state of large scale infrastructures, image acquisition by autonomous flight drone is efficient for stable angle and high quality image. Supervised learning requires a great deal of data set consisting images and annotation labels. It takes long time to accumulate images including damaged region of interest (ROI). In recent years, unsupervised deep learning approach such as generative adversarial network (GAN) for anomaly detection algorithms have progressed. When a damaged image is a generator input, it tends to reverse from the damaged state to the health-like state image. Using the distance of distribution between the real damaged image and the generated reverse aging health-like image, it is possible to detect the concrete damage automatically from unsupervised learning. This paper proposes an anomaly detection method using unpaired image-to-image translation mapping from damaged image to reverse aging fake like health condition. Actually, we apply our method to field studies, and we examine the usefulness for health monitoring concrete damages.