Advances and innovations in road surface inspection with light detection and ranging technology

Abstract

Light Detection and Ranging (LiDAR), an advanced non-contact sensing method capable of capturing 3D spatial data with up to millimeter-level precision depending on the ranging method, has been widely used in pavement defect detection and road asset management. This paper provides an overview of LiDAR-based pavement inspection techniques in terms of measurement principles, characterization of acquisition methods, and algorithmic processing of point cloud data. Subsequently, the characteristics of major LiDAR systems, including mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS), and their applicability for pavement information inspection are analyzed. MLS emerges as the predominant method due to its superior mobility and measurement precision in retrieving pavement data. Then, traditional and deep learning-based 3D point cloud processing algorithms are compared for pavement information inspection, challenges in achieving high accuracy and efficiency with large datasets are discussed, and future research directions are outlined in this study. Additionally, the paper highlights the practical outcomes achieved with economic LiDAR solutions, whose data densities are one to two orders of magnitude lower than those obtained with powerful and expensive solutions. Furthermore, the potential for integration with other technologies to enhance detection efficiency and precision is discussed.