TY - JOUR
T1 - Volumetric Pothole Detection from UAV-Based Imagery
AU - Chen, Siyuan
AU - Laefer, Debra F.
AU - Zeng, Xiangding
AU - Truong-Hong, Linh
AU - Mangina, Eleni
N1 - Publisher Copyright:
© 2024 American Society of Civil Engineers.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - 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.
AB - 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.
KW - Pavement evaluation
KW - Photogrammetry
KW - Point cloud
KW - Structure from motion (SfM)
KW - Unmanned aerial vehicle (UAV)
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U2 - 10.1061/JSUED2.SUENG-1458
DO - 10.1061/JSUED2.SUENG-1458
M3 - Article
AN - SCOPUS:85183869553
SN - 0733-9453
VL - 150
JO - Journal of Surveying Engineering
JF - Journal of Surveying Engineering
IS - 2
M1 - 05024001
ER -