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    Pothole Detection and Classification Using 3D Technology and Watershed Method

    Source: Journal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 002
    Author:
    Yi-Chang (James) Tsai
    ,
    Anirban Chatterjee
    DOI: 10.1061/(ASCE)CP.1943-5487.0000726
    Publisher: American Society of Civil Engineers
    Abstract: Potholes are one of the roadway distresses that negatively impact roadway safety. With emerging sensing technology, three-dimensional (3D) pavement data, derived using 3D laser technology, have become available for detecting cracking and rutting. This paper presents a pothole detection method using 3D pavement data and a watershed method. Tests using the 3D data collected on 10th Street, Atlanta, Georgia and 6 mi of roadway on U.S. 80, Savannah, Georgia, has shown a 94.97% accuracy, 90.80% precision, and 98.75% recall. It has been demonstrated that the proposed method is promising for pothole detection and can provide a reliable method for pothole detection, especially when 3D pavement data have been collected for crack detection and already available.
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      Pothole Detection and Classification Using 3D Technology and Watershed Method

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4245557
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    contributor authorYi-Chang (James) Tsai
    contributor authorAnirban Chatterjee
    date accessioned2017-12-30T13:05:52Z
    date available2017-12-30T13:05:52Z
    date issued2018
    identifier other%28ASCE%29CP.1943-5487.0000726.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4245557
    description abstractPotholes are one of the roadway distresses that negatively impact roadway safety. With emerging sensing technology, three-dimensional (3D) pavement data, derived using 3D laser technology, have become available for detecting cracking and rutting. This paper presents a pothole detection method using 3D pavement data and a watershed method. Tests using the 3D data collected on 10th Street, Atlanta, Georgia and 6 mi of roadway on U.S. 80, Savannah, Georgia, has shown a 94.97% accuracy, 90.80% precision, and 98.75% recall. It has been demonstrated that the proposed method is promising for pothole detection and can provide a reliable method for pothole detection, especially when 3D pavement data have been collected for crack detection and already available.
    publisherAmerican Society of Civil Engineers
    titlePothole Detection and Classification Using 3D Technology and Watershed Method
    typeJournal Paper
    journal volume32
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000726
    page04017078
    treeJournal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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