Accurately detecting pavement cracks is essential to apply preventive and effective pavement treatments in a timely manner. In this paper, we proposed the Dense Attention U-Net (DAU-Net) to achieve pixel-wise segmentation of cracks on 3D pavement images. The encoder of the DAU-Net consists of multi-stage dense blocks to improve its capability of extracting informative contextual features. To achieve precise localization of cracks in the decoder, a novel channel attention block (CAB) is proposed, which reduces noisy responses and highlight salient encoder features using the channel attention mechanism. The DAU-Net is evaluated on large-scale, real-world 3D asphalt pavement images. In the ablation study, the proposed CAB demonstrates its effectiveness with a large boost on crack segmentation precision. In the comparative study, the DAU-Net outperforms state-of-the-art semantic segmentation models from previous works. With both qualitative and quantitative evaluations, the effectiveness of the DAU-Net is verified.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DAU-Net: Dense Attention U-Net for Pavement Crack Segmentation


    Contributors:


    Publication date :

    2021-09-19


    Size :

    599394 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation

    Sun, Xinzi / Xie, Yuanchang / Jiang, Liming et al. | IEEE | 2022


    CrackFormer Network for Pavement Crack Segmentation

    Liu, Huajun / Yang, Jing / Miao, Xiangyu et al. | IEEE | 2023


    MorFormer: Morphology-Aware Transformer for Generalized Pavement Crack Segmentation

    Guo, Xin / Tang, Wenzhong / Wang, Haoran et al. | IEEE | 2025


    Deep Learning-Based Real-Time Crack Segmentation for Pavement Images

    Wang, Wenjun / Su, Chao | Springer Verlag | 2021


    CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network

    Han, Chengjia / Ma, Tao / Huyan, Ju et al. | IEEE | 2022