Abstract:
Currently, Unmanned Aerial Vehicles (UAVs) have been used in the most diverse applications
in both the civil and military sectors. In the civil sector, aerial inspection services
have been gaining a lot of attention, especially in the case of inspections of high voltage
electrical systems transmission lines. This type of inspection involves a helicopter carrying
three or more people (technicians, pilot, etc.) flying over the transmission line along its
entire length which is a dangerous service especially due to the proximity of the transmission
line and possible environmental conditions (wind gusts, for example). In this context,
the use of UAVs has shown considerable interest due to their low cost and safety for
transmission line inspection technicians. This work presents research results related to the
application of UAVs for transmission lines inspection, autonomously, allowing the identification
of invasions of the transmission line area as well as possible defects in components
(cables, insulators, connection, etc.) through the use of Convolutional Neural Networks
(CNN) for fault detection and identification. This thesis proposes the development of an
autonomous system to track power transmission lines using UAVs efficiently and with low
implementation and operation costs, based exclusively on rea-time image processing that
identifies the structure of the towers and transmission lines durin the flight and controls
the aircraft´s movements, guiding it along the closest possible path. A sumary of the work
developed will be presented in the next sections.
