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Road Pothole Detection System Based on Stereo Vision

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2018, Master of Sciences, Case Western Reserve University, EECS - Computer Engineering.
In this thesis, we propose a stereo vision system which detects potholes during driving. The objective is to benefit drivers to react to potholes in advance. This system contains two USB cameras taking photo simultaneously. We use parameters obtained from camera calibration with checkerboard to calculate the disparity map. 2-dimensional image points can be projected to 3-dimensional world points using the disparity map. With all the 3-dimensional points, we use the bi-square weighted robust least-squares approximation for road surface fitting. All points below the road surface model can be detected as pothole region. In case there are more than one pothole on the road, we use the connected component labelling algorithm to label pothole points into different potholes according to the 0 or 1 connection between pixels in binary images. The size and depth of each pothole can be obtained as well. The experiments we conducted show robust detection of potholes in different road and light conditions.
Christos Papachristou (Advisor)
Francis Merat (Committee Member)
Daniel Saab (Committee Member)
53 p.

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Citations

  • Li, Y. (2018). Road Pothole Detection System Based on Stereo Vision [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1525708920748809

    APA Style (7th edition)

  • Li, Yaqi. Road Pothole Detection System Based on Stereo Vision. 2018. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1525708920748809.

    MLA Style (8th edition)

  • Li, Yaqi. "Road Pothole Detection System Based on Stereo Vision." Master's thesis, Case Western Reserve University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1525708920748809

    Chicago Manual of Style (17th edition)