Anomaly detection in railway bridges using imaging techniques

The monitoring of the structural health of infrastructures is a very important topic in structural engineering, but unfortunately, there are few established techniques that are applicable in a wide range of situations. In this paper, we present a new method that adapts image analysis tools and methodologies, taken from the field of computer vision, and applies them to the monitoring signals of a railway bridge. We show that our method correctly identifies changes in the structural health of the bridge with very high precision, thus providing a better, simpler, and more general alternative to current methodologies used in the field.