Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor

Abstract

Guardrails are critical boundary infrastructures protecting against road departures and traffic collisions. The presence and condition information of in-service guardrails are essential for transportation agencies to perform necessary repair or replacement operations on time. Unfortunately, most current practices still rely on manual field surveys or windshield inspections that can be time-consuming, labor-intensive, and subjective. This study proposes an automated, network-level guardrail detection and tracking model based on 3D local features captured in mobile LiDAR data. The 3D local features, including corrugation, vertical profile, connectivity, and continuity of the guardrails, are introduced to extract guardrail status through four key sequential and corresponding steps, including Difference of Normals (DoN)-based segmentation, vertical profile-based filtering, guardrail-associated point re-population, and guardrail tracking. The proposed method is evaluated in two sections on State Route 113 and State Route 9 in Massachusetts. It shows promising performance with high precision rates of 95.6% and 95.5% and excellent length covering rates of 97.9% and 100.0%, respectively. The proposed method will provide a reliable and efficient means for transportation agencies to inspect and evaluate their critical guardrail infrastructure on a network level.