  The ability to automatically detect other vehicles on the road is vital to
the safety of partially-autonomous and fully-autonomous vehicles. Most of the
high-accuracy techniques for this task are based on R-CNN or one of its faster
variants. In the research community, much emphasis has been applied to using 3D
vision or complex R-CNN variants to achieve higher accuracy. However, are there
more straightforward modifications that could deliver higher accuracy? Yes. We
show that increasing input image resolution (i.e. upsampling) offers up to 12
percentage-points higher accuracy compared to an off-the-shelf baseline. We
also find situations where earlier/shallower layers of CNN provide higher
accuracy than later/deeper layers. We further show that shallow models and
upsampled images yield competitive accuracy. Our findings contrast with the
current trend towards deeper and larger models to achieve high accuracy in
domain specific detection tasks.
