Authors:
Jonathan Karlsson
1
;
Fredrik Strand
1
;
Josef Bigun
1
;
Fernando Alonso-Fernandez
1
;
Kevin Hernandez-Diaz
1
and
Felix Nilsson
2
Affiliations:
1
School of Information Technology (ITE), Halmstad University, Sweden
;
2
HMS Industrial Networks AB, Halmstad, Sweden
Keyword(s):
PPE, PPE Detection, Personal Protective Equipment, Machine Learning, Computer Vision, YOLO.
Abstract:
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.
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