Volume 41 Issue 6
Dec.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(6): 90-99
ZHANG Tao, WANG Jin, LIU Bin, XU Niuqi. Crack Segmentation of Asphalt Pavement Images Based on Improved U-net[J]. Journal of Transport Information and Safety, 2023, 41(6): 90-99. doi: 10.3963/j.jssn.1674-4861.2023.06.010
Citation: ZHANG Tao, WANG Jin, LIU Bin, XU Niuqi. Crack Segmentation of Asphalt Pavement Images Based on Improved U-net[J]. Journal of Transport Information and Safety, 2023, 41(6): 90-99. doi: 10.3963/j.jssn.1674-4861.2023.06.010
shu

Crack Segmentation of Asphalt Pavement Images Based on Improved U-net

doi: 10.3963/j.jssn.1674-4861.2023.06.010
  • 1.

    Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China

  • 2.

    Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China

  • Received Date: 2023-07-03
    Available Online: 2024-04-03
    Abstract
  • To improve the segmentation accuracy of image-based asphalt pavement cracks, this paper proposes a strip-attention-u-net (SAU) network based on U-net. The network uses ResNeSt50 as a core feature learning struc-ture to effectively capture semantic information and local details. A channel enhanced strip pooling (CESP) module in the encode-decode skip connection is investigated to enhance the ability of learning crack features and better utilize residual connections. A convolutional block attention (CBA) module in the up sampling stage of the decoder is developed to mitigate feature losses caused by channel compression and preserve crack features. A loss function comprised by a Dice Loss and a Focal Loss function is performed to attract thin and small crack features. A publicly available EdmCrack600 dataset and an experimental BJCrack600 dataset (600 asphalt pavement images collected in an experiment) are used to evaluate the performance of the SAU network. Ablation experiments are conducted and the SAU network is compared with state-of-the-art networks (FCN, PSPNet, DeepLabv3, U-net, Attention U-net, and U-net++). For EdmCrack600 dataset, the proposed SAU network outperforms the state-of-the-art networks, with intersection over union (IoU) and F1 score of 50.89% and 83.59%, respectively. Regarding the BJCrack600 dataset, the SAU network demonstrates the best performance among the state-of-the-art networks, achieving IoU and F1 score of 69.69% and 90.90%, respectively. The study findings could provide more intelligent and efficient supports in making advanced decisions of road maintenance.

     

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