Treffer: Combining Computer Vision and Drones for Proactive Construction Site Safety Monitoring.

Worksite accidents have long been the leading cause of occupational injuries and fatalities worldwide, primarily due to two factors: the open and dynamic nature of the worksite environment and the inadequacy and incompetence of onsite safety managers. Recent advancements in deep learning (DL) and computer vision (CV) offer promising solutions to long-standing challenges in construction safety management. This paper proposes a proactive, real-time monitoring model for construction site safety, inspired by recent research integrating unmanned aerial vehicles (UAVs) with DL-based CV techniques. Specially designed data matrix (DM) tags were affixed to the safety helmets and vests of workers. The model captures DM-tagged images on-site and applies DL-based image recognition algorithms to assess individual risk levels, thereby enabling the implementation of preventive safety measures. Preliminary experimental results show that the model achieved a recall of 97.3% and a precision of 98.3% in worker identification. These findings highlight the practical potential of the proposed approach. The study concludes with a discussion on how the proposed approach could be applied to future advancements in construction safety management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering, Project & Production Management is the property of Association of Engineering, Project & Production Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)