Volume 21 Issue 5
Nov.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(5): 309-322
YANG Wei, HUANG Li-hong, ZHAO Xiang-mo, WANG Xiao. Puddle area segmentation of asphalt pavements based on FRRN attention and supervision[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 309-322. doi: 10.19818/j.cnki.1671-1637.2021.05.026
Citation: YANG Wei, HUANG Li-hong, ZHAO Xiang-mo, WANG Xiao. Puddle area segmentation of asphalt pavements based on FRRN attention and supervision[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 309-322. doi: 10.19818/j.cnki.1671-1637.2021.05.026
shu

Puddle area segmentation of asphalt pavements based on FRRN attention and supervision

doi: 10.19818/j.cnki.1671-1637.2021.05.026
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    College of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2018YFC0807502

Key Project of National Natural Science Foundation of China Automobile Joint Fund Project U1864204

Natural Science Foundation Youth Project of Shaanxi Province 2017JQ6045

More Information
  • Author Bio:

    YANG Wei(1985-), male, assistant professor, PhD, yw@chd.edu.cn

  • Received Date: 2021-04-30
    Available Online: 2021-11-13
  • Publish Date: 2021-10-01
    Abstract
  • To realize the automatic segmentation of puddle area of asphalt roads under various brightness levels and climate conditions, an attentional supervision mechanism was developed on the upsampling layer of existing full resolution residual network (FRRN). A puddle area attention module based on the full-resolution residual network (PAAM-FRRN) model was established by adding the puddle area attention module (PAAM) and the deep supervision module. An encoder-decoder structure was constructed by max-pooling and upsampling to capture the visual characteristics of puddle in a global scale. An attentional supervision mechanism was introduced in the upsampling layer to conduct the upsampling for the puddle area and fuse the features of different network layers to minimize the loss function of each network layer and optimize the overall final loss of the network. A total of 1 770 images (750 under low light, 740 under strong light and 280 under rainy weather) of asphalt pavement with puddle were collected for the five-fold cross-validation test, and the segmentation results of the puddle area were obtained. Manual annotation results were used as truth values, and the dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), precision, recall, and Hausdorff distance (HD95) were used as quantitative evaluation indexes. The segmentation effect of the proposed model was compared with the FRRN and seven other representative traditional models. Research results show that the mean values of DSC, JSC, precision, and recall obtained by the proposed model are 0.91, 0.86, 0.92, and 0.93, respectively, which are 3.41%, 6.17%, 2.22%, and 4.49% higher than those obtained by the FRRN, and are all higher than those obtained by the seven traditional comparison models. Standard deviation values of DSC, JSC, precision, and recall as computed by the proposed model are 0.12, 0.15, 0.11, and 0.12, respectively, which are 20.00%, 16.67%, 21.43%, and 25.00% lower than those of FRRN, and are all lower than those of the seven traditional comparison models. The HD95 of the proposed model is 38.56, which is lower than those of the other comparison models. Therefore, the proposed model can achieve accurate and effective segmentation of puddle areas of asphalt pavements under different brightness levels and climate conditions. 6 tabs, 11 figs, 40 refs.

     

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