Mingqiao Han, Jihan Zhang, Yijun Huang, Jiwen Xu, Xi Chen, Ben M. Chen
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引用次数: 0
Abstract
Monitoring large-scale work sites is challenging, particularly in vast outdoor areas. Unmanned aerial vehicles (UAVs) provide an effective solution for site monitoring and worker management. This paper introduces a UAV-based framework integrated with digital twin (DT) modeling to enhance real-time data management and worker authorization verification. The pretrained YOLO-LCA model improved detection accuracy from 31.5% to 96.4%. The framework combines multi-object tracking with 3D site reconstruction, enabling precise global registration and situational awareness. Cross-referencing UAV detections with GPS-enabled worker IDs ensures that only authorized personnel are present, effectively identifying unapproved workers. The proposed framework has undergone large-scale validation across multiple construction projects in Hong Kong, demonstrating significant potential for modernizing work site management. By integrating UAVs and DT technology, this framework supports efficient monitoring, operational safety, and informed decision-making, providing a scalable approach to addressing the demands of large-scale construction site management.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.