Dzeng, R.-J.; Cheng, C.-W.; Cheng, C.-Y. A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality. Buildings2024, 14, 385.
Dzeng, R.-J.; Cheng, C.-W.; Cheng, C.-Y. A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality. Buildings 2024, 14, 385.
Dzeng, R.-J.; Cheng, C.-W.; Cheng, C.-Y. A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality. Buildings2024, 14, 385.
Dzeng, R.-J.; Cheng, C.-W.; Cheng, C.-Y. A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality. Buildings 2024, 14, 385.
Abstract
Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross bracing, which result in uneven loading and potential collapse, leading to casualties. In this study, we introduce a deep-learning-based, augmented reality (AR)-enabled system called scaffolding assembly deficiency detection system (SADDS) to assist field inspectors in identifying deficiencies in scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles (i.e., Microsoft HoloLens 2), for automated detection of deficiencies in scaffolding assemblies. The training test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for “qualified” frames and frames with the “missing cross-tie rod,” “missing lower-tie rod,” and “missing footboard” deficiencies, respectively. Field tests conducted at two construction sites demonstrated improved system performance compared to that in the training test. However, these field tests also revealed certain limitations of the SADDS system.
Keywords
building scaffolding; safety detection; AI; deep learning; AR; HoloLens; BIM
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
