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An approach for the automated extraction of road surface distress from a UAV-derived point cloud

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Date

2021

Author

Biçici, Serkan
Zeybek, Mustafa

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Citation

Biçici, S., & Zeybek, M. (2021). An approach for the automated extraction of road surface distress from a UAV-derived point cloud. Automation in Construction, 122, 103475.

Abstract

The condition of the road surface should be inspected to increase the service life of the road and to ensure safety and comfort. This study aims to automatically detect and measure road distress from unmanned aerial vehicle (UAV)-based images. The proposed methodology consists of three steps. First, images acquired from the UAV are used to generate the three-dimensional point cloud. Then, the road surface is extracted from the 3D point cloud. Finally, the developed algorithm is used to automatically detect and measure road distress. The accuracy assessment is conducted by comparing the analyses from point cloud data and measurements obtained from the traditional inspection method. The root mean square error values range from 2.09–6.72 cm. Finally, the outcomes of the proposed methodology are compared with those of commercial GIS software. Both produce statistically similar results for detecting road surface distress.

Source

Automation in Construction

Volume

122

URI

https://doi.org/10.1016/j.autcon.2020.103475
https://hdl.handle.net/11494/2500

Collections

  • İnşaat Mühendisliği Bölümü Koleksiyonu - Makaleler [4]
  • Scopus İndeksli Yayınlar Koleksiyonu [525]
  • WoS İndeksli Yayınlar Koleksiyonu [685]



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