This paper addresses the question of the detection of small
targets (vehicles) in ortho-images. This question differs from
the general task of detecting objects in images by several aspects.
Firstable, the vehicles to be detected are small, typically
smaller than 20x20 pixels. Secondly, due to the multifariousness
of the landscapes of the earth several pixel structures
similar to that of a vehicle might emerge (roof tops,
shadow patterns, rocks, buildings), whereas within the vehicle
class the inter-class variability is limited as they all look alike
from afar. Finally, the imbalance between the vehicles and
the rest of the picture is enormous in most cases. Specifically,
this paper is focused on the detection tasks introduced by the
VEDAI dataset [1]. This work supports an extensive study of
the problems one might face when applying deep neural networks
with low resolution and scarce data and proposes some
solutions. One of the contributions of this paper is a network
severely outperforming the state-of-the-art while being much
simpler to implement and a lot faster than competitive approaches.
We also list the limitations of this approach and
provide several new ideas to further improve our results.