  Detection of small-sized targets is of paramount importance in many aerial
vision-based applications. The commonly deployed low cost unmanned aerial
vehicles (UAVs) for aerial scene analysis are highly resource constrained in
nature. In this paper we propose a simple short and shallow network (SSSDet) to
robustly detect and classify small-sized vehicles in aerial scenes. The
proposed SSSDet is up to 4x faster, requires 4.4x less FLOPs, has 30x less
parameters, requires 31x less memory space and provides better accuracy in
comparison to existing state-of-the-art detectors. Thus, it is more suitable
for hardware implementation in real-time applications. We also created a new
airborne image dataset (ABD) by annotating 1396 new objects in 79 aerial images
for our experiments. The effectiveness of the proposed method is validated on
the existing VEDAI, DLR-3K, DOTA and Combined dataset. The SSSDet outperforms
state-of-the-art detectors in term of accuracy, speed, compute and memory
efficiency.
