• Find People
  • Campus Map
  • PiratePort
  • A-Z
    • About
    • Submit
    • Browse
    • Login
    View Item 
    •   ScholarShip Home
    • Dissertations and Theses
    • Master's Theses
    • View Item
    •   ScholarShip Home
    • Dissertations and Theses
    • Master's Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of The ScholarShipCommunities & CollectionsDateAuthorsTitlesSubjectsTypeDate SubmittedThis CollectionDateAuthorsTitlesSubjectsTypeDate Submitted

    My Account

    Login

    Statistics

    View Google Analytics Statistics

    TOWARDS A LOW-COST VISION SYSTEM FOR REAL-TIME PAVEMENT CONDITION ASSESSMENT

    View/ Open
    OLUFOWOBI-MASTERSTHESIS-2021.pdf (14.32Mb)

    Show full item record
    Author
    Olufowobi, Kenny
    Abstract
    Pavement condition assessment is typically performed through manual inspections and specialized hardware and software. Although advances in camera and sensing technology in the last decade helped propel the automation of pavement distress detection and characterization, increased equipment acquisition and running costs limit access to the most effective solutions. Furthermore, some of these advanced techniques require substantial human involvement to process and analyze data correctly. This thesis proposes a cost-effective, end-to-end automated approach to pavement condition assessment that employs a neural object detector to identify and measure instances of pavement distress in real time from oblique 2D imagery acquired using a UAV. To promote ease of implementation and scale, the associated modeling process is simplified by using Google Street View data as a proxy for data collected via a UAV-mounted camera. A state-of-the-art object detector architecture is applied to identify and localize pavement distress instances in monocular images. Camera data, information about Street View image acquisition conditions, and the principles of photogrammetry and planar homography are combined to construct a mapping for translating pixel distances to real-world distances. This capability is then integrated into the neural network inference process to derive an end-to-end system for real-time distress identification and measurement.
    URI
    http://hdl.handle.net/10342/9421
    Subject
     Object detection; planar homography; pavement condition assessment 
    Date
    2021-07-22
    Citation:
    APA:
    Olufowobi, Kenny. (July 2021). TOWARDS A LOW-COST VISION SYSTEM FOR REAL-TIME PAVEMENT CONDITION ASSESSMENT (Master's Thesis, East Carolina University). Retrieved from the Scholarship. (http://hdl.handle.net/10342/9421.)

    Display/Hide MLA, Chicago and APA citation formats.

    MLA:
    Olufowobi, Kenny. TOWARDS A LOW-COST VISION SYSTEM FOR REAL-TIME PAVEMENT CONDITION ASSESSMENT. Master's Thesis. East Carolina University, July 2021. The Scholarship. http://hdl.handle.net/10342/9421. August 12, 2022.
    Chicago:
    Olufowobi, Kenny, “TOWARDS A LOW-COST VISION SYSTEM FOR REAL-TIME PAVEMENT CONDITION ASSESSMENT” (Master's Thesis., East Carolina University, July 2021).
    AMA:
    Olufowobi, Kenny. TOWARDS A LOW-COST VISION SYSTEM FOR REAL-TIME PAVEMENT CONDITION ASSESSMENT [Master's Thesis]. Greenville, NC: East Carolina University; July 2021.
    Collections
    • Master's Theses
    Publisher
    East Carolina University

    xmlui.ArtifactBrowser.ItemViewer.elsevier_entitlement

    East Carolina University has created ScholarShip, a digital archive for the scholarly output of the ECU community.

    • About
    • Contact Us
    • Send Feedback