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    A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms

    Source: Journal of Performance of Constructed Facilities:;2023:;Volume ( 037 ):;issue: 006::page 04023052-1
    Author:
    Qingling Meng
    ,
    Jiabing Yang
    ,
    Yun Zhang
    ,
    Yilin Yang
    ,
    Jinbo Song
    ,
    Jing Wang
    DOI: 10.1061/JPCFEV.CFENG-4433
    Publisher: ASCE
    Abstract: Large numbers of bridges have already suffered various types of damage but still operate all year round without proper treatment. Conducted primarily manually, the routine bridge inspections are ineffective in detecting potential damage in time due to a lack of relevant instruments and equipment, particularly modern measures. In this study, a rapid and intelligent bridge inspection system that integrates multiple modules and deep learning algorithms was established. First, the robot inspection equipment is established. Then, the You Only Look Once version 3 (YOLOv3) object detection algorithm is employed to classify four types of defects from the acquired data. Finally, an image segmentation algorithm is used to identify crack defects at a pixel level. Experimental results reveal that the proposed system can be effectively applied to accurately locate defects (e.g., cracks, spalls, exposed tendons, and free lime) and identify cracks at a pixel level on various types of bridges without affecting traffic.
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      A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4296190
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    • Journal of Performance of Constructed Facilities

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    contributor authorQingling Meng
    contributor authorJiabing Yang
    contributor authorYun Zhang
    contributor authorYilin Yang
    contributor authorJinbo Song
    contributor authorJing Wang
    date accessioned2024-04-27T20:53:40Z
    date available2024-04-27T20:53:40Z
    date issued2023/12/01
    identifier other10.1061-JPCFEV.CFENG-4433.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296190
    description abstractLarge numbers of bridges have already suffered various types of damage but still operate all year round without proper treatment. Conducted primarily manually, the routine bridge inspections are ineffective in detecting potential damage in time due to a lack of relevant instruments and equipment, particularly modern measures. In this study, a rapid and intelligent bridge inspection system that integrates multiple modules and deep learning algorithms was established. First, the robot inspection equipment is established. Then, the You Only Look Once version 3 (YOLOv3) object detection algorithm is employed to classify four types of defects from the acquired data. Finally, an image segmentation algorithm is used to identify crack defects at a pixel level. Experimental results reveal that the proposed system can be effectively applied to accurately locate defects (e.g., cracks, spalls, exposed tendons, and free lime) and identify cracks at a pixel level on various types of bridges without affecting traffic.
    publisherASCE
    titleA Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms
    typeJournal Article
    journal volume37
    journal issue6
    journal titleJournal of Performance of Constructed Facilities
    identifier doi10.1061/JPCFEV.CFENG-4433
    journal fristpage04023052-1
    journal lastpage04023052-14
    page14
    treeJournal of Performance of Constructed Facilities:;2023:;Volume ( 037 ):;issue: 006
    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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