Evaluation of UAV Photogrammetric capability in Road Pavement Cracks Detection

Document Type : Research Article


1 دانشجو دکتری

2 Department of Engineering, University of Tehran, Tehran, Iran

3 Department of Engineering, K. N. Toosi University of Technology, Tehran, Iran



To establish a system for managing road pavement, it is mandatory to prepare information components based on various perspectives of pavement management. One of the most significant information components in these systems is quality assessment regarding road pavement status. Accordingly, in this regard, data containing details of surface pavement failures and defects are of great significance. Apart from causing vehicle depreciation & damage, maintenance costs, and reducing the useful lifespan of the pavement structure, road pavement failures also lead to accidents and reduce road safety. Bearing in mind that the most important surface damages in road pavement are related to cracks with longitudinal, transverse, oblique, alligator, and block types, and as such cracks and defects can be visually and non-destructively assessed and evaluated, imaging-based approaches and techniques can provide details such as the type of defect, its severity, extent, and location and prove to be highly useful. In this paper, the Unmanned Aerial Vehicle (UAV)/drone photogrammetry has been proposed as a complementary approach aimed at providing information on defects caused by cracks in the country's road pavement management system. According to the author, the output of UAV photogrammetric products will significantly improve if the system parameters are adjusted. Consequently, through presenting a procedure to investigate the optimal parameters in the design of a UAV photogrammetric network, further, attempts were made for the implementation of an automated algorithm based on image processing operations & classifier decision tree which is independent of scale and image dimensions. Hence, after removing the road edges and determining the asphalt area, a pixel detection operation was carried out to reveal the cracks. Furthermore, after preparing the ground reality through selected orthophoto mosaic, the evaluation of crack pixel detection was determined using the proposed algorithm with three methods. An accuracy of 96% was determined for the main orthophoto mosaic. For the test orthophotos, which were the result of images taken by Phantom 4 Pro and Mavic Pro at different altitudes, an accuracy of approximately 82% to 91% was determined.


Main Subjects

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