Artificial Intelligence and Mobile Phone-Based Pavement Marking Condition Assessment and Litter Identification

Pavement marking, as a transportation asset, is highly related to safety and mobility but has a short service life. Faded pavement marking presents a significant concern for road users, compromising their ability to navigate safely. Additionally, litter (e.g., vehicle debris) on the roadways poses a substantial hazard that can significantly contribute to traffic accidents. To address these issues, regular inspection and maintenance of the pavement are necessary, including repainting the faded markings and cleaning the litter on the roadways, to ensure the pavement is in good, clean, and safe condition. However, traditional inspection methods still heavily rely on manual efforts, which are subjective, labor-intensive, and time-consuming, which are not suitable for large-scale and frequent implementation. The advancements in artificial intelligence (AI), particularly deep learning and computer vision, have provided new solutions to inspect transportation infrastructure. However, there is limited application in assessing the conditions of pavement markings and identifying roadway litter. Also, counting and locating the identified issues are less involved in prior studies. Therefore, this AI-based project aims to develop a lightweight, affordable, and automated approach to inspect pavement marking conditions and pavement cleanliness, facilitating efficient planning maintenance work and ultimately improving the safety of road users.


  • English


  • Status: Active
  • Funding: $100000
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Center for Transformative Infrastructure Preservation and Sustainability

    North Dakota State University
    Fargo, ND  United States  58108
  • Project Managers:

    Tolliver, Denver

  • Performing Organizations:

    University of Utah

    Department of Civil and Environmental Engineering
    110 Central Campus Drive Suite 2000
    Salt Lake City, UT  United States  84112
  • Principal Investigators:

    Chen, Jianli

  • Start Date: 20240506
  • Expected Completion Date: 20260505
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Source Data: CTIPS-007

Subject/Index Terms

Filing Info

  • Accession Number: 01920047
  • Record Type: Research project
  • Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
  • Contract Numbers: 69A3552348308
  • Files: UTC, RIP
  • Created Date: May 29 2024 10:19AM