  In recent years, a lot of attention is paid to deep learning methods in the
context of vision-based construction site safety systems, especially regarding
personal protective equipment. However, despite all this attention, there is
still no reliable way to establish the relationship between workers and their
hard hats. To answer this problem a combination of deep learning, object
detection and head keypoint localization, with simple rule-based reasoning is
proposed in this article. In tests, this solution surpassed the previous
methods based on the relative bounding box position of different instances, as
well as direct detection of hard hat wearers and non-wearers. The results show
that the conjunction of novel deep learning methods with humanly-interpretable
rule-based systems can result in a solution that is both reliable and can
successfully mimic manual, on-site supervision. This work is the next step in
the development of fully autonomous construction site safety systems and shows
that there is still room for improvement in this area.
