The Automated Extraction of Parking Lot Pavement Distress from UAV SfM Point Clouds

Abstract

The road surface maintenance of parking areas is important to maintaining public infrastructure and extending its service life. Concave surface rutting detection is the first step in maintenance procedures and has become a tough task because of the increasing number of parking areas. UAVs were employed to collect data from the parking lot’s surface, providing a faster and more cost-effective solution rather than traditional detection methods. Furthermore, the algorithm of Structure from Motion (SfM) was utilized to reconstruct the 3D point cloud of the target area. RANSAC and DBSCAN algorithms were used to extract the road distress, which was further analyzed. The results illustrated that the proposed method can accurately detect and classify parking damage, achieving an accuracy rate of 90%.