  Maintaining aging infrastructure is a challenge currently faced by local and
national administrators all around the world. An important prerequisite for
efficient infrastructure maintenance is to continuously monitor (i.e., quantify
the level of safety and reliability) the state of very large structures.
Meanwhile, computer vision has made impressive strides in recent years, mainly
due to successful applications of deep learning models. These novel progresses
are allowing the automation of vision tasks, which were previously impossible
to automate, offering promising possibilities to assist administrators in
optimizing their infrastructure maintenance operations. In this context, the
IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity
for deep learning and computer vision researchers to get involved and help
accurately track pavement damages on road networks. This paper proposes two
contributions to that topic: In a first part, we detail our solution to the RDD
Challenge. In a second part, we present our efforts in deploying our model on a
local road network, explaining the proposed methodology and encountered
challenges.
