A review of advanced pavement distress evaluation techniques using unmanned aerial vehicles

ABSTRACTThe economy of the nation is significantly influenced by pavement management systems. The use of cutting-edge techniques and technology in various pavement management system applications, as well as research into these topics, are particularly important today. To reduce human errors in data collection, advances in technology and automated surveillance should be used. The most recent methods used in modern equipment for the automatic evaluation of pavement deterioration are reviewed in this paper. To identify trending tools, research gaps, emerging technologies, challenges and limitations of using computer vision for pavement distress and condition assessment, papers collected using UAV (Unmanned Ariel Vehicle) data collection and the application of machine learning methods are investigated. The review comes to the conclusion that the application of machine learning techniques is the general trend in evaluating the condition of pavements, despite some limitations not only in the detection of a small number of pavement distresses with complex patterns but also in the indication of the severity and density of distresses, opening up possibilities for further study.

• Record URL:
  • https://doi.org/10.1080/10298436.2023.2268796
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Manjusha, M
  • Sunitha, V
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 2268796
• Serial:
  • International Journal of Pavement Engineering
  • Volume: 24
  • Issue Number: 2
  • Publisher: Taylor & Francis
  • ISSN: 1029-8436
  • Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html

Subject/Index Terms

• TRT Terms: Data collection; Detection and identification systems; Drones; Evaluation; Machine learning; Pavement distress
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01911255
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 10 2024 4:16PM