Road condition monitoring utilizing UAV photogrammetry aligned to the principal curve of the mine haul truck path

Mine haul roads degrade rapidly due to extreme loads on suboptimal construction materials. Unmanned aerial vehicles (UAV) are suited to quantify large-area road-network conditions, such as surface roughness, defects and grade to optimize remediation of poor conditions and reduce overall costs. Mine haul roads present unique challenges, such as material type and edge characteristics, to automatic road detection that often fails, requiring manual road input. This research work proposes a new method using the road center determined by the principal curve of a haul truck’s path. Analysis grids were created from this center line. A dense point cloud from UAV photogrammetry was generated and multiple linear regression analysis was conducted on each individual grid. The root-mean-square error in each grid indicates the surface roughness, and the change of slope between grids indicates the road grade inconsistencies. This method was applied to 26 road sections, and the results were validated by images taken from the truck operator’s vantage point. Critical defects were identified including excessive pothole formation, corrugation, depressions retaining water, and narrowing of travel lane. The results demonstrate this is a valid method of road identification and quantification of road defects at a mine site.