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Pavement Surface Distress Detection and Evaluation Using Image Processing Technology

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2011, Master of Science, University of Toledo, Electrical Engineering.
Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. The cameras used by most of the AIAS are based on Charge-Coupled Device (CCD) image sensors where a visible ray is projected. However, the quality of the images captured by the CCD cameras was limited by the inconsistent illumination and shadows caused by sunlight. To enhance the CCD image quality, a high-power artificial lighting system has been used, which requires a complicated lighting system and a significant power source. In this thesis, we will first introduce a high-efficiency and economical approach for pavement distress inspection by using laser imaging. After the pavement images are captured, regions corresponding to potholes are represented by a matrix of square tiles and the estimated shape of the pothole is determined. The vertical, horizontal distress measure, the total number of distress tiles and the depth index information are calculated and input into a three-layer feed-forward neural network for pothole severity and crack type classification. We also introduced an adaptive pavement distress segmentation method based on Genetic Algorithm, which can dynamically locate the optical threshold in the search space. The proposed analysis algorithms are capable of enhancing the pavement image, extracting the pothole from background and analyzing its severity. To validate the system, actual pavement pictures were taken from pavements both in highway and local roads. The experimental results demonstrated that the proposed model works well for pothole and crack detection.
Ezzatollah Salari, PhD (Advisor)
Junghwan Kim, PhD (Committee Member)
Jackson Carvalho, PhD (Committee Member)
84 p.

Recommended Citations

Citations

  • Yu, X. (2011). Pavement Surface Distress Detection and Evaluation Using Image Processing Technology [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302032254

    APA Style (7th edition)

  • Yu, Xinren. Pavement Surface Distress Detection and Evaluation Using Image Processing Technology. 2011. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302032254.

    MLA Style (8th edition)

  • Yu, Xinren. "Pavement Surface Distress Detection and Evaluation Using Image Processing Technology." Master's thesis, University of Toledo, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302032254

    Chicago Manual of Style (17th edition)