A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection. In this paper, an efficient deep network called CrackNet-V is proposed for automated pixel-level crack detection on 3D asphalt pavement images. Compared with the original CrackNet, CrackNet-V has a deeper architecture but fewer parameters, resulting in improved accuracy and computation efficiency. Inspired by CrackNet, CrackNet-V uses invariant spatial size through all layers such that supervised learning can be conducted at pixel level. Following the VGG network, CrackNet-V uses $3\times 3$ size of filters for the first six convolutional layers and stacks several $3\times 3$ convolutional layers together for deep abstraction, resulting in reduced number of parameters and efficient feature extraction. CrackNet-V has 64113 parameters and consists of ten layers, including one pre-process layer, eight convolutional layers, and one output layer. A new activation function leaky rectified tanh is proposed in this paper for higher accuracy in detecting shallow cracks. The training of CrackNet-V was completed after 3000 iterations, which took only one day on a GeForce GTX 1080Ti device. According to the experimental results on 500 testing images, CrackNet-V achieves a high performance with a Precision of 84.31%, Recall of 90.12%, and an F-1 score of 87.12%. It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet. The efficiency of CrackNet-V further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V


    Beteiligte:
    Fei, Yue (Autor:in) / Wang, Kelvin C. P. (Autor:in) / Zhang, Allen (Autor:in) / Chen, Cheng (Autor:in) / Li, Joshua Q. (Autor:in) / Liu, Yang (Autor:in) / Yang, Guangwei (Autor:in) / Li, Baoxian (Autor:in)


    Erscheinungsdatum :

    01.01.2020


    Format / Umfang :

    5488820 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Transverse Cracking of Curved Asphalt Pavement Highways

    Abdel Nabi, R. M. / Abdelalim, A. M. / Shalaby, A. et al. | British Library Conference Proceedings | 1996


    A Semi-Supervised Learning Approach for Pixel-Level Pavement Anomaly Detection

    Ren, Ruiqi / Shi, Peixin / Jia, Pengjiao et al. | IEEE | 2023


    Primitive-Based Classification of Pavement Cracking Images

    Koutsopoulos, H. N. / Downey, A. B. | British Library Online Contents | 1993


    Primitive-Based Classification of Pavement Cracking Images.

    Koutsopoulos, H.N. | Online Contents | 1993