  Workplace injuries are common in today's society due to a lack of adequately
worn safety equipment. A system that only admits appropriately equipped
personnel can be created to improve working conditions. The goal is thus to
develop a system that will improve workers' safety using a camera that will
detect the usage of Personal Protective Equipment (PPE). To this end, we
collected and labeled appropriate data from several public sources, which have
been used to train and evaluate several models based on the popular YOLOv4
object detector. Our focus, driven by a collaborating industrial partner, is to
implement our system into an entry control point where workers must present
themselves to obtain access to a restricted area. Combined with facial identity
recognition, the system would ensure that only authorized people wearing
appropriate equipment are granted access. A novelty of this work is that we
increase the number of classes to five objects (hardhat, safety vest, safety
gloves, safety glasses, and hearing protection), whereas most existing works
only focus on one or two classes, usually hardhats or vests. The AI model
developed provides good detection accuracy at a distance of 3 and 5 meters in
the collaborative environment where we aim at operating (mAP of 99/89%,
respectively). The small size of some objects or the potential occlusion by
body parts have been identified as potential factors that are detrimental to
accuracy, which we have counteracted via data augmentation and cropping of the
body before applying PPE detection.
