An automated method for progress monitoring of under-ground pipe installation sites using image color analysis of iPhone LiDAR camera data
Tsukasa Mizutan, Shunsuke Iwai · IOP Conference Series Earth and Environmental Science · 2024
Abstract This research presents an innovative method for automated progress monitoring at underground pipe installation sites. The method leverages the LiDAR camera of an iPhone to capture detailed point cloud data of construction sites. Through sophisticated image color analysis, the method accurately distinguishes between piped and non-piped areas within excavations. Key aspects of the proposed workflow include the segmentation of excavation areas, differentiation of main and side excavations, and application of an earth color mask to isolate pipes in RGB space. The study focuses on enhancing the precision of measurements like excavation width, depth, and pipe burial depth, addressing challenges in traditional construction site monitoring. The developed algorithm significantly reduces the manual labor traditionally required for dimensional measurements and construction volume evaluation, offering an efficient, cost-effective solution. Empirical tests demonstrate the method’s capability in drastically improving measurement accuracy, labor costs, and processing time in construction site monitoring. This approach represents a significant stride in the digital transformation of the construction industry, leveraging readily available smartphone technology to streamline complex monitoring tasks.