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.
| Original language | English |
|---|---|
| Article number | 409 |
| Journal | ISPRS International Journal of Geo-Information |
| Volume | 8 |
| Issue number | 9 |
| DOIs | |
| State | Published - 12 Sep 2019 |
Keywords
- Digital surface model
- Point cloud
- Region growing algorithm
- Road distress detection
- UAV
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