  In order to monitor the state of large-scale infrastructures, image
acquisition by autonomous flight drones is efficient for stable angle and
high-quality images. Supervised learning requires a large data set consisting
of images and annotation labels. It takes a long time to accumulate images,
including identifying the damaged regions of interest (ROIs). In recent years,
unsupervised deep learning approaches such as generative adversarial networks
(GANs) for anomaly detection algorithms have progressed. When a damaged image
is a generator input, it tends to reverse from the damaged state to the healthy
state generated image. Using the distance of distribution between the real
damaged image and the generated reverse aging healthy state fake 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 images to reverse aging fakes
that approximates healthy conditions. We apply our method to field studies, and
we examine the usefulness of our method for health monitoring of concrete
damage.
