Volume 24 Issue 3
Jun.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(3): 154-170
GUAN Jin-chao, DING Ling, YANG Xu, LIU Peng-fei, WANG Hai-nian. Pavement surface distress detection in complex scenarios driven by multi-dimensional image fusion[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 154-170. doi: 10.19818/j.cnki.1671-1637.2024.03.010
Citation: GUAN Jin-chao, DING Ling, YANG Xu, LIU Peng-fei, WANG Hai-nian. Pavement surface distress detection in complex scenarios driven by multi-dimensional image fusion[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 154-170. doi: 10.19818/j.cnki.1671-1637.2024.03.010
shu

Pavement surface distress detection in complex scenarios driven by multi-dimensional image fusion

doi: 10.19818/j.cnki.1671-1637.2024.03.010
  • 1.

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

  • 2.

    Zhejiang Expressway Co., Ltd., Hangzhou 310020, Zhejiang, China

  • 3.

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

  • 4.

    School of Future Transportation, Chang'an University, Xi'an 710064, Shaanxi, China

  • 5.

    Institute of Highway Engineering, RWTH Aachen University, Aachen 52074, North Rhine-Westphalia, Germany

Funds:

National Key Research and Development Program of China 2021YFB2601000

National Natural Science Foundation of China 52078049

More Information
  • Author Bio:

    GUAN Jin-chao(1995-), male, postdoctor, guan@chd.edu.cn

    DING Ling(1986-), female, assistant professor, PhD, dingling@chd.edu.cn

  • Received Date: 2024-01-19
    Available Online: 2024-07-18
  • Publish Date: 2024-06-30
    Abstract
  • To improve the accuracy and robustness of crack and pothole detection of pavement surface in complex scenarios, the morphological irregularity of pavement surface distresses and the influence of environmental noises in practical detection scenarios were considered, and an automatic pavement surface distress segmentation model and feature fusion optimization method for multi-dimensional images were proposed. Based on high-precision pavement surface point cloud models reconstructed by multi-view stereo vision, 2D and 3D images were generated by the rasterization of homologous point clouds. The pavement surface distress image dataset in complex scenarios was established. A lightweight encoding-decoding network, namely PDU-net, integrating depthwise separable convolution and multi-layer feature combination, was developed for pixel-level crack and pothole detection. Based on the segmentation model, two multi-dimensional image fusion strategies, including pixel operation and channel recombination, were proposed to improve the extraction efficiency of deep learning networks in shallow and fine crack features. Experimental results show that the PDU-net model can effectively learn features from different types of images and distresses. The training loss of the PDU-net on different datasets can converge stably, with the training cycles of 3D images shorter than that of 2D images. Compared with existing convolutional segmentation networks, the PDU-net model achieves higher accuracy and efficiency for pavement surface distress segmentation in complex scenarios. The harmonic means of 3D crack and pothole image segmentation are 81.00% and 95.85%, respectively. The average forward inference time of the PDU-net is about 30% of the existing models. The segmentation accuracy and robustness of complex cracks can be improved by multi-dimensional fusion images. When the optimal color-depth ratio is 0.2, the harmonic mean of the crack segmentation increases to 83.31%. In conclusion, the proposed method can effectively suppress environmental noises and strengthen the surface distress features in complex scenarios.

     

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