Citation

Abidin MSZ, Ariff MFM. J. Adv. Geospat. Sci. Technol. 2023; 3(1): 1-14.

Copyright

(Copyright © 2023, Universiti Teknologi Malaysia Press)

DOI

10.11113/jagst.v3n1.58

PMID

unavailable

Abstract

Road defect detection is essential to road maintenance to prevent traffic accidents. In Malaysia, road conditions are one of the leading causes of road accidents that may cause fatalities and injuries. In Malaysia, between 2010 and 2019, the number of road accidents increased from 414,421 cases in 2010 to 567,516 cases in 2019. Unmanned Aerial Vehicles (UAV) are helpful instruments that may be utilised to obtain accurate data for road defect mapping. The purposes of this study are (i) to investigate road distress using low-altitude photogrammetry by generating orthophoto from low-altitude UAV photogrammetry in detecting road defects and (ii) to assess the accuracy of road defects from orthophoto and 3D model point clouds. The benefits of utilising UAVs include high flexibility, low cost, easy manoeuvrability and minimal field work. The results of this study show that orthophoto is very suitable for classifying road defects. Furthermore, the rise in road defects is caused by congested roads near construction and industrial regions, with many trucks passing through the study area. The results demonstrate that the large crack area can be successfully analysed using 3D point clouds, but a narrower crack with roughness features is complicated and challenging to be spotted.

Language: en

Keywords

Orthophoto; Point clouds; road defects; UAV