A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

Runmin Zhao, Yi Huang, Haoyuan Luo (), Xiaoming Huang and Yangzezhi Zheng
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Runmin Zhao: School of Transportation, Southeast University, Nanjing 211189, China
Yi Huang: School of Transportation, Southeast University, Nanjing 211189, China
Haoyuan Luo: School of Transportation, Southeast University, Nanjing 211189, China
Xiaoming Huang: School of Transportation, Southeast University, Nanjing 211189, China
Yangzezhi Zheng: School of Transportation, Southeast University, Nanjing 211189, China

Sustainability, 2023, vol. 15, issue 3, 1-26

Abstract: In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the needs of continuous road image capture and pavement disease recognition. Furthermore, to realize automatic route planning and control, continuous photography control, and image stitching and smoothing tasks, a UAV control framework for road damage detection, based on the Dijkstra algorithm, the speeded-up robust features (SURF) algorithm, the random sampling consistency (RANSAC) algorithm, and the gradual in and out weight fusion method, was also proposed in this paper. With the Canny operator, it was verified that the road stitched long image obtained by the UAV control method proposed in this paper is applicable to machine learning pavement disease identification.

Keywords: unmanned aerial vehicle (UAV); road damage detection; continuous photography; route planning algorithm; image stitching method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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