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

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.

Automated Pavement Construction Inspection Using Uncrewed Aerial Systems (UAS)—Hot Mixed Asphalt (HMA) Temperature Segregation

“…While several recent studies [17][18][19][20][21][22][23][24][25][26][27][28] have investigated the application of a UAS on pavement distress detection, there is limited research on utilizing a UAS for HMA thermal segregation inspection. For instance, Du et al [23] reviewed the application of digital image processing tools on pavement distress detection, including thermal segregation.…”

Assessment of asphalt pavement aging condition based on GF-2 high-resolution remote sensing image

“…With the increasing maturity of unmanned aerial vehicle ground observation technology, there is an increasing amount of research combining it with deep learning for road distress identification. [13][14][15][16][17] Researchers have used drone aerial images to accurately identify and classify pavement defects, such as potholes and cracks. [18][19][20][21] They can also extract and calculate parameters, such as length, width, and area of the defects, 22,23 with recognition accuracy reaching centimeter 24 or millimeter 25 levels.…”