A Scaffolding Assembly Deficiency Detection System with Deep Learning and Augmented Reality

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. This research introduces a novel approach employing deep learning (i.e., YOLO v5) and augmented reality (AR), termed the scaffolding assembly deficiency detection system (SADDS), designed to aid field inspectors in discerning deficiencies within scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles, for the automated identification of deficiencies in scaffolding assemblies. The conducted 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. Subsequent field tests conducted at two construction sites demonstrated improved system performance compared to the training test. Furthermore, the advantages and disadvantages of employing mobile phones and AR goggles were discussed, elucidating certain limitations of the SADDS system, such as self-occlusion and efficiency issues.