Optics and Precision Engineering, Volume. 31, Issue 9, 1357(2023)

Multi-lane line detection and tracking network based on spatial semantics segmentation

Jinpeng SHI and Xu ZHANG*
Author Affiliations
  • School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, Shanghai201620, China
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    Figures & Tables(12)
    Structure of SCNNLane network
    Image preprocessing
    Overall architecture of VGG16-SCNN
    Structure of spatial convolution model
    Decode output images of lane line
    Structure of coupling network
    Decode output images
    Encoding partial visualization
    Output results of SCNNLane network after training
    Comparison of visualization results under Tusimple dataset
    • Table 1. Environment configuration parameters of software and hardware

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      Table 1. Environment configuration parameters of software and hardware

      CategoryNameConfiguration
      HardwareCPUAMD Ryzen 7 5800X8-Corn Processor 3.80 GHz
      GPUNVIDA GeForce RTX3070Ti
      SoftwareCUDA version10.1
      Python version3.9
      Pytorch version1.7.1
      OpenCV version4.4.0
    • Table 2. Comparison of results with different methods on Tusimple dataset

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      Table 2. Comparison of results with different methods on Tusimple dataset

      MethodsAccFNFP
      LaneNet96.387.802.44
      VGG-LaneNet94.0310.211.0
      SPNet92.417.755.12
      YOLACT1695.366.893.27
      Blend Mask1794.617.203.75
      Ours97.124.302.13
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    Jinpeng SHI, Xu ZHANG. Multi-lane line detection and tracking network based on spatial semantics segmentation[J]. Optics and Precision Engineering, 2023, 31(9): 1357

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    Paper Information

    Category: Information Sciences

    Received: Jul. 13, 2022

    Accepted: --

    Published Online: Jun. 6, 2023

    The Author Email: Xu ZHANG (zxu1116@126.com)

    DOI:10.37188/OPE.20233109.1357

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