Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.


    Access

    Download


    Export, share and cite



    Title :

    Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features


    Contributors:
    Weidong Song (author) / Guohui Jia (author) / Hong Zhu (author) / Di Jia (author) / Lin Gao (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Recognition of asphalt pavement crack length using deep convolutional neural networks

    Tong, Zheng / Gao, Jie / Han, Zhenqiang et al. | British Library Online Contents | 2018


    Automated shape-based pavement crack detection approach

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

    Free access

    Deep Domain Adaptation for Pavement Crack Detection

    Liu, Huijun / Yang, Chunhua / Li, Ao et al. | IEEE | 2023