@ARTICLE{Wójcik_Bartosz_A_2023,
 author={Wójcik, Bartosz and Żarski, Mateusz and Książek, Kamil and Miszczak, Jarosław A. and Skibniewski, Mirosław J.},
 volume={71},
 number={6},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e147340},
 howpublished={online},
 year={2023},
 abstract={In recent years, a lot of attention has been paid to deep learning methods in the context of vision-based construction site safety systems. However, there is still more to be done to establish the relationship between supervised construction workers and their essential personal protective equipment, like hard hats. A deep learning method combining object detection, head center localization, and 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 and direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the approaches mentioned above, with class-specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that using deep learning methods with a humanly interpretable rule-based algorithm is better suited for detecting hard hat non-wearers.},
 type={Article},
 title={A deep learning method for hard-hat-wearing detection based on head center localization},
 URL={http://ochroma.man.poznan.pl/Content/128931/PDF/BPASTS-03833-EA.pdf},
 doi={10.24425/bpasts.2023.147340},
 keywords={hard hat, personal protective equipment, construction safety, deep learning, keypoint R-CNN},
}