01523nas a2200181   4500000000100000000000100001008004100002260001200043100002000055700002000075700002000095700002200115700002600137245009200163856007700255490001200332520099700344       2023                            d  c10/20231 aBartosz Wójcik1 aMateusz Żarski1 aKamil Książek1 aJaroslaw Miszczak1 aMirosław Skibniewski00aA deep learning method for hard-hat-wearing detection based on head center localization  uhttps://journals.pan.pl/dlibra/publication/147340/edition/128931/content0 v71 (EA)3 a<p>In recent years, a lot of attention is 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.</p>