Abstract:

A small unmanned aircraft system (sUAS) or drone has proven to be a valuable tool for civil infrastructure inspection, highway inspection, unpaved road inspection, bridge inspection, construction work progress monitoring, and other applications. Additionally, several proof-of-concept studies showed that sUAS could be helpful for airfield pavement distress detection. This report documents a comprehensive study that evaluated the usefulness of sUAS-collected data in detection and rating both asphalt concrete and Portland cement concrete pavement distresses. Multiple sUASs were deployed at different altitudes to collect data from six airports in Michigan, Illinois, Iowa, and New Jersey. The collected data were then processed to create red, green, and blue (RGB) orthophotos, Digital Elevation Models (DEMs), hillshades from DEMs, and stereo-thermal orthophotos to assess what resolution and methods were best to detect and rate each type of airfield pavement distress. Based on the subsequent analysis, 1.5-mm/pix resolution for RGB orthophotos and a 6.0-mm/pix resolution for DEMs are recommended to detect and rate airfield pavement distresses. During data collection and processing, recommended standard processes were established, such as the use of high-quality ground control points (GCPs) for data acquisition, a maximum distance of 100 m between two GCPs, and a minimum three-person data collection team. Additionally, it is recommended to ensure the usability of flight-control software prior to data collection and the scheduling of sUAS deployment during low wind speed and precipitation free weather conditions. With these recommended practices in place, sUAS data can be collected effectively, safely, and efficiently. In addition, using the recommended data resolutions, most severity levels of airfield asphalt and Portland concrete cement distresses could be identified and rated.