• Opto-Electronic Engineering
  • Vol. 49, Issue 5, 210378 (2022)
Chong Zhang, Yingping Huang*, Zhiyang Guo, and Jingyi Yang
Author Affiliations
  • School of Optical-Electronic and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    DOI: 10.12086/oee.2022.210378 Cite this Article
    Chong Zhang, Yingping Huang, Zhiyang Guo, Jingyi Yang. Real-time lane detection method based on semantic segmentation[J]. Opto-Electronic Engineering, 2022, 49(5): 210378 Copy Citation Text show less
    Framework of the method
    Fig. 1. Framework of the method
    Framework of the method
    Fig. 1. Framework of the method
    The parameters of the network and illustration of LaneConv and LaneDeconv
    Fig. 2. The parameters of the network and illustration of LaneConv and LaneDeconv
    Depth separable convolution. (a) Channel by channel convolution; (b) Pointwise convolution
    Fig. 3. Depth separable convolution. (a) Channel by channel convolution; (b) Pointwise convolution
    (a) Laneconv structure; (b) Lanedeconv structure
    Fig. 4. (a) Laneconv structure; (b) Lanedeconv structure
    (a) Channel attention; (b) Spatial attention
    Fig. 5. (a) Channel attention; (b) Spatial attention
    DBSCAN cluster
    Fig. 6. DBSCAN cluster
    The output in different stages. (a) Binary output; (b) Clustering output; (c) Fitting output
    Fig. 7. The output in different stages. (a) Binary output; (b) Clustering output; (c) Fitting output
    Comparison between visualization results of baseline and our method on TuSimple. (a) Original scene; (b) True value; (c) Lanenet results; (d) Results of our method
    Fig. 8. Comparison between visualization results of baseline and our method on TuSimple. (a) Original scene; (b) True value; (c) Lanenet results; (d) Results of our method
    Comparison of effects before and after adding CBAM. (a) Not joined CBAM; (b) Joined CBAM
    Fig. 9. Comparison of effects before and after adding CBAM. (a) Not joined CBAM; (b) Joined CBAM
    Visual results generated by our method on some of typical scenarios
    Fig. 10. Visual results generated by our method on some of typical scenarios
    NameParametersComputations
    3*3 Conv9C29HWC2
    LaneConv3C29HWC2/8
    2*2 DeConv4C24HWC2
    LaneDeConv7C2/4 7HWC2/4
    Table 1. Comparison of parameters and computations
    方法Acc/(%)FP/(%)FN/(%)Speed/(f/s)mIoU/(%)
    注意:表中的N/A表示相关论文未提及或无法复制该项目,Method 1加入了新引入的卷积结构但未加入CBAM,Method 2使用普通卷积结构但加入了CBAM,Method 3同时加入了CBAM和新引入的卷积结构
    基于检测的方法PointLaneNet96.344.675.1871.0N/A
    PolyLaneNet93.369.429.33115.0N/A
    基于分割的方法SCNN96.536.171.807.557.37
    VGG-LaneNet94.0310.211.01.741.34
    LaneNet94.429.09.062.556.59
    Method 194.349.18.4102.456.08
    Method 295.708.34.3158.665.22
    Method 395.648.54.4598.764.46
    Table 2. Comparison results with other methods on tusimple dataset
    Chong Zhang, Yingping Huang, Zhiyang Guo, Jingyi Yang. Real-time lane detection method based on semantic segmentation[J]. Opto-Electronic Engineering, 2022, 49(5): 210378
    Download Citation