Recognition of temporary vertical objects in large high-quality point clouds of construction sites

Although adherence to project schedule is the most critical performance metric among project owners, still 53 % of typical construction projects exhibit schedule delays. While construction progress monitoring is key to allow effective project management, it is still a largely manual, error prone and inefficient process. To contribute to more efficient construction progress monitoring, this research proposes a method to automatically detect the most common temporary object classes in large-scale laser scanner point clouds of construction sites. Finding the position of these objects in the point cloud can help determine the current state of construction progress and verify compliance with safety regulations. The proposed workflow includes a combination of several techniques: image processing over vertical projections of point clouds, finding patterns in 3D detected contours, and performing checks over vertical cross-sections with deep learning methods. After applying and testing the method on three real-world point clouds and testing with three object categories (cranes, scaffolds, and formwork), the results reveal that our technique achieves rates above 88 % for precision and recall and outstanding computational performance. These metrics demonstrate the method’s capability to support the automatic 3D object detection in point clouds of construction sites.