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UAV Photogrammetry-Based 3D Road Distress Detection

by Yumin Tan * and Yunxin Li
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 409; https://doi.org/10.3390/ijgi8090409
Received: 20 May 2019 / Revised: 28 August 2019 / Accepted: 4 September 2019 / Published: 12 September 2019
(This article belongs to the Special Issue Geospatial Monitoring with Hyperspatial Point Clouds)
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. View Full-Text
Keywords: UAV; road distress detection; digital surface model; point cloud; region growing algorithm UAV; road distress detection; digital surface model; point cloud; region growing algorithm
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Tan, Y.; Li, Y. UAV Photogrammetry-Based 3D Road Distress Detection. ISPRS Int. J. Geo-Inf. 2019, 8, 409.

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