A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN) is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN) in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Automatic Pavement Crack Recognition Based on BP Neural Network


    Beteiligte:
    Li Li (Autor:in) / Lijun Sun (Autor:in) / Guobao Ning (Autor:in) / Shengguang Tan (Autor:in)


    Erscheinungsdatum :

    2014




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Pavement Crack Automatic Recognition Based on Wiener Filtering

    Zhang, J. / Sha, A. / Sun, Z. Y. et al. | ASCE | 2009


    Pavement Crack Automatic Recognition Based on Wiener Filtering

    Zhang, J. / Sha, A. / Sun, Z.Y. et al. | British Library Conference Proceedings | 2009


    Review on Automatic Pavement Crack Image Recognition Algorithms

    Peng, Bo / Jiang, Yang-sheng / Pu, Yun | ASCE | 2015


    Pavement Crack Recognition Based Wireless Video Sensors

    Zeng, Linhui / Shi, Xiaofa / Li, Yichao et al. | ASCE | 2015


    Automatic Pavement Crack Identification Based on an Improved C-Mask Region-Based Convolutional Neural Network Model

    Xiao, Liyang / Li, Wei / Deng, Nanyi et al. | Transportation Research Record | 2022