Improving construction site efficiency through automated progress monitoring of underground pipe installation sites using image color analysis of iPhone LiDAR camera data - Tsukasa Mizutani; Shunsuke Iwai (Developments in the Built Environment)
This study presents an innovative method utilizing smartphone for automated progress monitoring at underground pipe installation sites. Leveraging the LiDAR iPhone camera, the method captures detailed point cloud data of construction sites. Sophisticated color analysis of images accurately distinguishes between areas with and without pipes within excavations. Key aspects of the proposed workflow include segmentation of the excavation area, differentiation between main and side excavations, and application of an earth color mask in the RGB space to isolate pipes. The research focuses on enhancing measurement precision for excavation width, depth, and pipe burial depth, significantly reducing the manual labor traditionally required at construction sites, thereby offering an efficient and cost-effective solution. We further demonstrated the robustness of the proposed algorithm by applying it to two types of data acquired at actual construction sites. This approach is expected to contribute significantly to the digital transformation in the construction industry.