Raster-Based Point Cloud Mapping of Defective Road Marking: Toward Automated Road Inspection via Airborne LiDAR

Abstract

The integrity of road markings has a significant impact on driving safety, especially in the emerging automated driving scenario. Road markings are susceptible to wearing damage; it is therefore important for road agencies to examine and maintain markings periodically. This study presents a novel method for detection of road marking defect via an unmanned aerial vehicle (UAV)–laser radar (LiDAR) platform. The key idea is to evaluate the damage rate of road markings by point reflectivity. The method consists of three major steps: point cloud registration, marking segmentation, and defective marking detection. A high-intensity prioritized raster method is proposed to extract whole marking regions within defect area, and the damage rate of markings is defined to evaluate damage severity. Field test results show that the precision and recall of marking extraction are over 90%, and the precision of marking defect detection is over 93%. The failed detections were attributed to subjectivity of ground truth and neglect of small defects.