  Knowledge over the number of animals in large wildlife reserves is a vital
necessity for park rangers in their efforts to protect endangered species.
Manual animal censuses are dangerous and expensive, hence Unmanned Aerial
Vehicles (UAVs) with consumer level digital cameras are becoming a popular
alternative tool to estimate livestock. Several works have been proposed that
semi-automatically process UAV images to detect animals, of which some employ
Convolutional Neural Networks (CNNs), a recent family of deep learning
algorithms that proved very effective in object detection in large datasets
from computer vision. However, the majority of works related to wildlife
focuses only on small datasets (typically subsets of UAV campaigns), which
might be detrimental when presented with the sheer scale of real study areas
for large mammal census. Methods may yield thousands of false alarms in such
cases. In this paper, we study how to scale CNNs to large wildlife census tasks
and present a number of recommendations to train a CNN on a large UAV dataset.
We further introduce novel evaluation protocols that are tailored to censuses
and model suitability for subsequent human verification of detections. Using
our recommendations, we are able to train a CNN reducing the number of false
positives by an order of magnitude compared to previous state-of-the-art.
Setting the requirements at 90% recall, our CNN allows to reduce the amount of
data required for manual verification by three times, thus making it possible
for rangers to screen all the data acquired efficiently and to detect almost
all animals in the reserve automatically.
