  Detecting Personal Protective Equipment in images and video streams is a
relevant problem in ensuring the safety of construction workers. In this
contribution, an architecture enabling live image recognition of such equipment
is proposed. The solution is deployable in two settings -- edge-cloud and
edge-only. The system was tested on an active construction site, as a part of a
larger scenario, within the scope of the ASSIST-IoT H2020 project. To determine
the feasibility of the edge-only variant, a model for counting people wearing
safety helmets was developed using the YOLOX method. It was found that an
edge-only deployment is possible for this use case, given the hardware
infrastructure available on site. In the preliminary evaluation, several
important observations were made, that are crucial to the further development
and deployment of the system. Future work will include an in-depth
investigation of performance aspects of the two architecture variants.
