Title
Volumetric Pothole Detection from UAV-Based Imagery
Author
Chen, Siyuan (University College Dublin; Hunan Institute of Science and Technology)
Laefer, Debra F. (New York University; University College Dublin)
Zeng, Xiangding (Hunan Institute of Science and Technology)
Truong-Hong, Linh (TU Delft Optical and Laser Remote Sensing)
Mangina, Eleni (University College Dublin)
Date
2024
Abstract
Road networks are essential elements of a community's infrastructure and need regular inspection. Present practice requires traffic interruptions and safety risks for inspectors. The road detection system based on vehicle-mounted lasers is also quite mature, offering advantages such as high-precision defect detection, high automation, and fast detection speed. However, it does have drawbacks such as high equipment procurement and maintenance costs, limited flexibility, and insufficient coverage range. Therefore, this paper proposes a low-cost unmanned aerial vehicle (UAV)-based alternative using imagery for automatic road pavement inspection focusing on pothole detection and classification. A slicing-based method, entitled the Pavement Pothole Detection Algorithm, is applied to the imagery after it is converted into a three-dimensional point cloud. When compared with manually extracted results, the proposed UAV-structure-from-motion (SfM) method and the associated algorithm achieved 0.01 m level accuracy for pothole depth detection and maximum errors of 0.0053 m3 in volume evaluation for cases studies of both a road and a bridge deck.
Subject
Pavement evaluation
Photogrammetry
Point cloud
Structure from motion (SfM)
Unmanned aerial vehicle (UAV)
To reference this document use:
http://resolver.tudelft.nl/uuid:fc0cc2b0-b2e5-4c69-a32c-fac5d145b70b
DOI
https://doi.org/10.1061/JSUED2.SUENG-1458
Embargo date
2024-07-27
ISSN
0733-9453
Source
Journal of Surveying Engineering, 150 (2)
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 Siyuan Chen, Debra F. Laefer, Xiangding Zeng, Linh Truong-Hong, Eleni Mangina