  Unmanned Aerial Vehicles (UAVs) have recently shown great performance
collecting visual data through autonomous exploration and mapping in building
inspection. Yet, the number of studies is limited considering the post
processing of the data and its integration with autonomous UAVs. These will
enable huge steps onward into full automation of building inspection. In this
regard, this work presents a decision making tool for revisiting tasks in
visual building inspection by autonomous UAVs. The tool is an implementation of
fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack
detection. It offers an optional mechanism for task planning of revisiting
pinpoint locations during inspection. It is integrated to a quadrotor UAV
system that can autonomously navigate in GPS-denied environments. The UAV is
equipped with onboard sensors and computers for autonomous localization,
mapping and motion planning. The integrated system is tested through
simulations and real-world experiments. The results show that the system
achieves crack detection and autonomous navigation in GPS-denied environments
for building inspection.
