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Automated Pavement Distress Detection Using Advanced Image Processing Techniques

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2009, Master of Science in Engineering, University of Toledo, Engineering.
In this thesis, a novel, fast and self-adaptive image processing method is proposed for the extraction and connection of break points of cracks in pavement images. The algorithm first finds the initial point of a crack and then determines the crack’s classification into transverse and longitudinal types. Different search algorithms are used for different types of cracks. Then the algorithm traces along the crack pixels to find the break point and then connect the identified crack point to the nearest break point in the particular search area. The nearest point then becomes the new initial point and the algorithm continues the process until reaching the end of the crack. The experimental results show that this connection algorithm is very effective in maximizing the accuracy of crack identification.
Ezzatollah Salari (Advisor)
Eddie Chou (Committee Member)
Mohsin Jamali (Committee Member)
87 p.

Recommended Citations

Citations

  • Sun, Y. (2009). Automated Pavement Distress Detection Using Advanced Image Processing Techniques [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1260820545

    APA Style (7th edition)

  • Sun, Yao. Automated Pavement Distress Detection Using Advanced Image Processing Techniques. 2009. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1260820545.

    MLA Style (8th edition)

  • Sun, Yao. "Automated Pavement Distress Detection Using Advanced Image Processing Techniques." Master's thesis, University of Toledo, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1260820545

    Chicago Manual of Style (17th edition)