  Service robots can help with many of our daily tasks, especially in those
cases where it is inconvenient or unsafe for us to intervene: e.g., under
extreme weather conditions or when social distance needs to be maintained.
However, before we can successfully delegate complex tasks to robots, we need
to enhance their ability to make sense of dynamic, real world environments. In
this context, the first prerequisite to improving the Visual Intelligence of a
robot is building robust and reliable object recognition systems. While object
recognition solutions are traditionally based on Machine Learning methods,
augmenting them with knowledge based reasoners has been shown to improve their
performance. In particular, based on our prior work on identifying the
epistemic requirements of Visual Intelligence, we hypothesise that knowledge of
the typical size of objects could significantly improve the accuracy of an
object recognition system. To verify this hypothesis, in this paper we present
an approach to integrating knowledge about object sizes in a ML based
architecture. Our experiments in a real world robotic scenario show that this
combined approach ensures a significant performance increase over state of the
art Machine Learning methods.
