  Visuals captured by high-flying aerial drones are increasingly used to assess
biodiversity and animal population dynamics around the globe. Yet, challenging
acquisition scenarios and tiny animal depictions in airborne imagery, despite
ultra-high resolution cameras, have so far been limiting factors for applying
computer vision detectors successfully with high confidence. In this paper, we
address the problem for the first time by combining deep object detectors with
super-resolution techniques and altitude data. In particular, we show that the
integration of a holistic attention network based super-resolution approach and
a custom-built altitude data exploitation network into standard recognition
pipelines can considerably increase the detection efficacy in real-world
settings. We evaluate the system on two public, large aerial-capture animal
datasets, SAVMAP and AED. We find that the proposed approach can consistently
improve over ablated baselines and the state-of-the-art performance for both
datasets. In addition, we provide a systematic analysis of the relationship
between animal resolution and detection performance. We conclude that
super-resolution and altitude knowledge exploitation techniques can
significantly increase benchmarks across settings and, thus, should be used
routinely when detecting minutely resolved animals in aerial imagery.
