Smartphone Based Traffic Sign Inventory and Assessment

Road signs are an important part of the infrastructure and are needed to ensure smooth and safe traffic flow. Faded, occluded, damaged or vandalized signs can confuse or misinform drivers and lead to unsafe driving behavior. E.g. if a driver is not able to see a stop sign, he or she might drive into an intersection without stopping and cause an accident. Government agencies are tasked with maintaining good signage and part of it is regular inspections to detect problems. Current methods involve manual inspections, specialized vehicles, or citizen reports. They are tedious, expensive, or not always reliable. In this project the project team developed a traffic sign inventory and assessment system that built on their smartphone based road inspection system. In previous years the Navlab group had developed a road inspection system that is based on a vehicle mounted smartphone. The project team has published a description of the system and details of the computer vision techniques used to analyze the images. The smartphone is mounted on the windshield and is powered by cigarette lighter (Figure 1 left). While the vehicle is driving the smartphone collects images or videos of the outside and tags them with time, global positioning system (GPS), and other selected information. One of the key ideas behind the collection system is that it can be easily mounted on any vehicle, especially those that drive on the roads on a regular basis, e.g. garbage trucks drive through every neighborhood once a week. It is therefore possible to collect data frequently without the need for a dedicated vehicle or a dedicated driver. The images can be displayed in the asset management system of the department or with free software. An example is shown in Figure 1 (right) where the data is displayed on Google Earth. This will allow the user to inspect the road from a computer instead of physically going to the road. In the first version of the system the project team automatically detected road distress by using computer vision algorithms to find road cracks in the images. In this second version the project team extended it to detect road signs, specifically stop signs. The project team choose this after receiving feedback from the city of Pittsburgh that traffic signs and graffiti are important problems. Traffic signs are significant parts of the road infrastructure and they need to be inspected and maintained. It is an important safety concern to detect degradation or obstruction as early as possible. Graffiti is for the most part a nuisance problem that is difficult for the City to address because the general public usually does not report it and regular inspections are too costly. The best method to defeat it is to remove it as soon as possible so that the intended exposure to the public is denied. Detecting graffiti in images is a very challenging machine vision problem. As a first step the project team wants to detect graffiti or other vandalism on traffic signs. Those are not only a nuisance but also safety concerns and they are easier to detect because it is known a-priori how a clean traffic sign should look like.

  • Supplemental Notes:
    • Contract to a Performing Organization has not yet been awarded.


  • English


  • Status: Completed
  • Sponsor Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Project Managers:

    Ehrlichman, Courtney

  • Principal Investigators:

    Mertz, Christoph

  • Start Date: 20160101
  • Expected Completion Date: 0
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01595813
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Files: UTC, RiP
  • Created Date: Apr 8 2016 2:15PM