Efficient Vertical Object Detection in Large High-Quality Point Clouds of Construction Sites

Abstract: Even when adherence to the project schedule is a critical performance metric, still 53% of construction projects exhibit schedule delays. To contribute to efficient construction progress monitoring, a method is proposed to detect temporary objects in scans of construction sites. The proposed workflow includes: image processing, computer vision, and deep learning techniques. The method was tested on three real scans and with three object categories (cranes, scaffolds, and formwork). It achieved average rates above 88% for precision and recall and outstanding computational performance (1s to process 10^5points). These metrics demonstrate the method’s capability to segment point clouds of construction sites.