  In this study a model pipeline is proposed that combines computer vision with
control-theoretic methods and utilizes low cost sensors. The proposed work
enables perception-aware motion control for a quadrotor UAV to detect and
navigate to objects of interest such as wind turbines and electric towers. The
distance to the object of interest was estimated utilizing RGB as the primary
sensory input. For the needs of the study, the Microsoft AirSim simulator was
used. As a first step, a YOLOv8 model was integrated providing the basic
position setpoints towards the detection. From the YOLOv8 inference, a target
yaw angle was derived. The subsequent algorithms, combining performant in
computational terms computer vision methods and YOLOv8, actively drove the
drone to measure the height of the detection. Based on the height, an estimate
of the depth was retrieved. In addition to this step, a convolutional neural
network was developed, namely ActvePerceptionNet aiming at active YOLOv8
inference. The latter was validated for wind turbines where the rotational
motion of the propeller was found to affect object confidence in a near
periodical fashion. The results of the simulation experiments conducted in this
study showed efficient object height and distance estimation and effective
localization.
