An accurate and reliable pavement crack detection system plays an important role in evaluating pavement condition and providing needed information for decision making for pavement maintenance and rehabilitation. Among existing crack detection methods, the seed-based image segmentation method has proved to be fast and efficient for automated crack detection. However, its performance is not stable under varying conditions. This paper proposes an extended and optimized seed-based crack detection method after an extensive review of current practices. The proposed method included two main steps. In the first step, pavement images were preprocessed. Lane marking was masked to be a noncrack area and the nonuniform background of the images was corrected. In the second step, crack seeds were detected through grid cell analysis and then connected through a Euclidean minimum spanning tree construction. In addition, undesirable small objects, such as minimal branches and noises, were removed by a path length–based removal method. The proposed algorithm was evaluated by using 105 pavement images collected with the Texas Department of Transportation VCrack system. The experiment results showed that the proposed method could accurately and efficiently detect cracks in the images.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Seed-Based Approach for Automated Crack Detection from Pavement Images


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2016-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    FoSA: F* Seed-growing Approach for crack-line detection from pavement images

    Li, Q. / Zou, Q. / Zhang, D. et al. | British Library Online Contents | 2011


    Automated shape-based pavement crack detection approach

    Teng Wang / Kasthurirangan Gopalakrishnan / Omar Smadi et al. | DOAJ | 2018

    Free access

    Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

    Weidong Song / Guohui Jia / Hong Zhu et al. | DOAJ | 2020

    Free access

    Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system

    Lettsome, C.A. / Tsai, Y.-C. / Kaul, V. | British Library Online Contents | 2012


    Mathematical Morphology Based Asphalt Pavement Crack Detection

    Wei, Na / Zhao, Xiangmo / Wang, Tao et al. | ASCE | 2009