Ricerc@Sapienza

Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries.
The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs;
all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years,
different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and
overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings
of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric
algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for
crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some
preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete
material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the
transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired
using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery
and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wired, cables and tile borders).