Computer-Vision Model for Estimation of Road Sign Retro-Reflectivity Based on Deep Learning Algorithm and Vehicle Built-in Cameras

The US Department of Transportation requires road signs transportation agencies maintain sign retro reflectivity such that signs have the same shape and color day and night. Regularly checking road sign retro-reflectivity values ensures timely replacement of road signs with inadequate retro-reflectivity, enhancing road users’ visibility and safety. In this project, the research team address the practical safety and cost concern regarding road sign replacement strategy. They seek to apply deep-learning techniques and computer vision models for retro reflectivity detection using built-in-vehicle technologies. The research team proposes a technique to estimate the amounts of retro-reflectivity from the road signs using deep learning algorithms. This proposed research is part of the team’s agenda to employ affordable methods to improve road safety.

Language

• English

Project

• Status: Active
• Funding: $72067
• Contract Numbers:

  69A3551747117

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

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

  South Carolina State University

  300 College Street NE
  Orangeburg, South Carolina  United States  29117

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Managing Organizations:

  Center for Connected Multimodal Mobility

  Clemson University
  Clemson, SC  United States  29634
• Performing Organizations:

  South Carolina State University

  300 College Street NE
  Orangeburg, South Carolina  United States  29117

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204
• Principal Investigators:

  Mwakalonge, Judith

• Start Date: 20230501
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Computer vision; Equipment replacement; Highway safety; Machine learning; Reflective signs; Retroreflectivity; Traffic signs
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01908257
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Feb 14 2024 5:09PM