Hard hat wearing detection based on head keypoint localization
This work addresses safety monitoring on construction sites, representing an incremental improvement in vision-based systems for personal protective equipment detection.
The paper tackled the problem of reliably detecting whether construction workers are wearing hard hats by combining deep learning, object detection, head keypoint localization, and rule-based reasoning, resulting in a solution that surpassed previous methods in tests.
In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems, especially regarding personal protective equipment. However, despite all this attention, there is still no reliable way to establish the relationship between workers and their hard hats. To answer this problem a combination of deep learning, object detection and head keypoint localization, with 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, as well as direct detection of hard hat wearers and non-wearers. The results show that the conjunction of novel deep learning methods with humanly-interpretable rule-based systems can result in a solution that is both reliable and can successfully mimic manual, on-site supervision. This work is the next step in the development of fully autonomous construction site safety systems and shows that there is still room for improvement in this area.