Abstract
The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.
Highlights
Unmanned aerial vehicle (UAV) photogrammetry found its applications in many fields due to the rapid development of both unmanned aerial vehicles (UAVs) photogrammetry hardware and software
UAVs have the advantages of high flexibility, relatively low cost compared to survey vehicles, easy maneuverability, and less field work [7]; they are highly promising in pavement condition assessment
This paper proposed an automatic, efficient, and low-cost method to detect road surface distress using UAV photogrammetry images
Summary
Unmanned aerial vehicle (UAV) photogrammetry found its applications in many fields due to the rapid development of both UAV photogrammetry hardware and software. The traditional manual inspection of roads is highly time-consuming, labor-intensive, and subjective [3] Some automated methods, such as different kinds of road survey vehicles equipped with stereo cameras, light detection and ranging (LiDAR) technology, laser profilers, etc., were developed and deployed in road surveys, which could greatly improve the efficiency and objectivity of the survey [4,5,6]. UAVs have the advantages of high flexibility, relatively low cost compared to survey vehicles, easy maneuverability, and less field work [7]; they are highly promising in pavement condition assessment. In the study of Inzerillo et al [39], pavement distress was accurately replicated in a model produced by the SfM algorithm, demonstrating the credibility of UAV image-derived 3D models in road condition surveys
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