Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management

Transportation asset management requires timely information collection to inform relevant maintenance practices. Traditional data collection methods often necessitate manual operation or the use of specialized equipment, e.g., light detection and ranging (LiDAR), which can be labor-intensive and costly to implement. With advancements in computing techniques, artificial intelligence (AI) has emerged as a powerful tool for automatically detecting objects in images and videos. Therefore, this project aims to develop an implementable AI package that streamlines the inspection of transportation assets through automated processes. Specifically, a smartphone was mounted on the vehicle’s front windshield to record videos of transportation assets on both highways and local roads in Utah. These videos were then converted and processed into labeled images, which served as training and test datasets for the AI algorithms. Based on a deep learning framework, i.e., You Only Look Once (YOLO), the authors developed accurate and efficient AI algorithms to automatically detect and identify transportation assets, which include pavement marking issues, traffic signs, litter and trash, and steel guardrails and concrete barriers. The developed AI models demonstrate good performance in identifying targeted objects, achieving over 85% accuracy. The AI package developed in this project is expected to enable timely and efficient information collection for transportation assets, thereby improving road safety.

• Record URL:
  • https://www.ugpti.org/resources/reports/details.php?id=1178
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/77215
• Summary URL:
  • https://rosap.ntl.bts.gov/view/dot/77213
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Utah, Salt Lake City

  Department of Civil and Environmental Engineering
  Salt Lake City, UT  United States 

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Utah Department of Transportation

  Salt Lake City, UT  United States 
• Authors:
  • Kuang, Biao
  • Chen, Jianli
• Publication Date: 2024-7

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 48p

Subject/Index Terms

• TRT Terms: Artificial intelligence; Asset management; Automatic data collection systems; Detection and identification system applications; Highway maintenance; Machine learning; Maintenance management; Smartphones
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01929747
• Record Type: Publication
• Report/Paper Numbers: MPC 24-533, MPC-668
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Sep 6 2024 4:58PM