  In this work we consider the task of detecting sheep onboard an unmanned
aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep
are relatively small, only about 15 pixels across. Although deep learning
strategies have gained enormous popularity in the last decade and are now
extensively used for object detection in many fields, state-of-the-art
detectors perform poorly in the case of smaller objects. We develop a novel
dataset of UAV imagery of sheep and consider a variety of object detectors to
determine which is the most suitable for our task in terms of both accuracy and
speed. Our findings indicate that a UNet detector using the weighted Hausdorff
distance as a loss function during training is an excellent option for
detection of sheep onboard a UAV.
