  Infrastructure inspection is a very costly task, requiring technicians to
access remote or hard-to-reach places. This is the case for power transmission
towers, which are sparsely located and require trained workers to climb them to
search for damages. Recently, the use of drones or helicopters for remote
recording is increasing in the industry, sparing the technicians this perilous
task. This, however, leaves the problem of analyzing big amounts of images,
which has great potential for automation. This is a challenging task for
several reasons. First, the lack of freely available training data and the
difficulty to collect it complicate this problem. Additionally, the boundaries
of what constitutes a damage are fuzzy, introducing a degree of subjectivity in
the labelling of the data. The unbalanced class distribution in the images also
plays a role in increasing the difficulty of the task. This paper tackles the
problem of structural damage detection in transmission towers, addressing these
issues. Our main contributions are the development of a system for damage
detection on remotely acquired drone images, applying techniques to overcome
the issue of data scarcity and ambiguity, as well as the evaluation of the
viability of such an approach to solve this particular problem.
