Using Computer Vision and Deep Learning Techniques to Extract Roadway Geometry from Aerial Images

The overall goal of this project is to develop computer vision tools to extract different roadway geometry data such as school zone markings, lane configurations (i.e., turning lanes lengths, and lane, shoulder and median widths), presence of signals (i.e., identification of signal poles), and sidewalks (i.e., presence or absence of sidewalks) from high resolution aerial images, which can be used by FDOT planners and engineers at various levels of traffic operations and safety analysis. Consistent with this goal, the main objectives of this project are to: (a) examine how traffic data collection can leverage emerging computer vision techniques, in particular, image processing, deep learning, machine learning, and artificial intelligence to develop statewide roadway inventory lists; (b) design an automated signalized intersection geometric data extraction algorithm based on high-resolution images in order to identify roadway geometry data such as school zone markings, lane configurations (i.e., turning lanes and their lengths), and sidewalks (i.e., presence or absence of sidewalks) from high resolution aerial images, and (c) generate a geographic information services (GIS)-based inventory list of these roadway geometry features for the entire state of Florida including ON and OFF roadways. This is an innovative solution that employs the computer vision technology to potentially replace traditional manual inventory, which is labor intensive and prone to errors.

    Language

    • English

    Project

    • Status: Active
    • Funding: $223843
    • Contract Numbers:

      BED30 977-02

    • Sponsor Organizations:

      Florida Department of Transportation

      Research Center
      605 Suwannee Street MS-30
      Tallahassee, FL  United States  32399-0450
    • Managing Organizations:

      Department of Transpotation

      605 Suwannee Ste
      Tallahassee, FL  United States  32399
    • Project Managers:

      EI-Urfali, Alan

    • Performing Organizations:

      Florida State University, Tallahassee

      217 Westcott Building
      Tallahassee, FL  United States  32306-
    • Principal Investigators:

      Ozguven, Eren

    • Start Date: 20220330
    • Expected Completion Date: 20230228
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01841196
    • Record Type: Research project
    • Source Agency: Florida Department of Transportation
    • Contract Numbers: BED30 977-02
    • Files: RIP, STATEDOT
    • Created Date: Mar 30 2022 2:24PM