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基于扩张卷积金字塔网络的车道线检测算法

田晟 张剑锋 张裕天 许凯

田晟, 张剑锋, 张裕天, 许凯. 基于扩张卷积金字塔网络的车道线检测算法[J]. 西南交通大学学报, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026
引用本文: 田晟, 张剑锋, 张裕天, 许凯. 基于扩张卷积金字塔网络的车道线检测算法[J]. 西南交通大学学报, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026
TIAN Sheng, ZHANG Jianfeng, ZHANG Yutian, XU Kai. Lane Detection Algorithm Based on Dilated Convolution Pyramid Network[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026
Citation: TIAN Sheng, ZHANG Jianfeng, ZHANG Yutian, XU Kai. Lane Detection Algorithm Based on Dilated Convolution Pyramid Network[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 386-392, 416. doi: 10.3969/j.issn.0258-2724.20181026

基于扩张卷积金字塔网络的车道线检测算法

doi: 10.3969/j.issn.0258-2724.20181026
基金项目: 国家留学基金(201706155003);广东省科技计划(2015A080803001)
详细信息
    作者简介:

    田晟(1969—),男,副教授,博士,研究方向为交通运输工程,E-mail:shitian1@scut.edu.cn

  • 中图分类号: TN911.73

Lane Detection Algorithm Based on Dilated Convolution Pyramid Network

  • 摘要: 为满足汽车高级驾驶辅助系统对车道线检测准确性和时效性的要求,采用改进的ResNet50网络作为基础模型提取局部车道线特征,利用扩张卷积能指数级扩大感受野的特点,设计了扩张卷积金字塔模块,用以完整提取不同尺度的车道线特征,提出“锚点栅格”的思想,将输出划分为一组栅格,对每个栅格进行分类和回归分析,经过非极大值抑制等后处理,最终输出车道线标记点集. 结果表明:在CULane多场景数据集里对模型进行测试,在交并比阈值取为0.3的评估条件下其综合评估指标F-measure达到78.6%,检测速率达到40帧/s,在评估指标相近的情况下具有远高于空间卷积神经网络(spatial convolutional neural networks,SCNN)模型的检测速率,并在眩光、弯道等困难场景中的检测效果优于SCNN.

     

  • 图 1  标准卷积与扩张卷积

    Figure 1.  Standard convolution and dilated convolution

    图 2  扩张卷积金字塔网络模型

    Figure 2.  Dilated convolutional pyramid network model

    图 3  扩张卷积感受野

    Figure 3.  Receptive filed of dilated convolution

    图 4  车道线回归模型

    Figure 4.  Lane line regression model

    图 5  基于IoU的评估方法

    Figure 5.  Evaluation method based on IoU

    表  1  两种模型在CULane上的评估结果

    Table  1.   Evaluation results of two models on CULane %

    场景SCNN扩张卷积金字塔网络
    PprecisionPrecallPF-measurePprecisionPrecallPF-measure
    标准 95.6/90.8 95.5/90.4 95.7/90.6 92.4/87.8 94.2/89.5 93.3/88.7
    拥挤 80.8/70.4 79.1/68.9 79.9/69.7 78.9/70.5 78.5/70.1 78.7/70.3
    眩光 74.6/60.4 70.8/56.6 72.6/58.4 73.0/62.6 74.4/63.8 73.7/63.2
    阴影 81.7/67.3 80.9/66.7 81.3/67.0 76.6/68.0 78.8/70.0 77.7/69.0
    无标线 60.3/45.7 54.6/41.3 57.3/43.4 56.6/46.4 54.9/45.0 55.7/45.7
    箭头 92.5/85.5 89.5/82.7 91.0/84.1 87.2/82.3 86.8/81.9 87.0/82.1
    弯道 84.6/70.2 72.0/59.7 77.8/64.5 83.6/68.9 84.1/69.3 83.8/69.1
    夜晚 78.3/67.4 75.2/64.8 76.7/66.1 73.4/64.6 74.8/65.8 74.1/65.2
    平均值 81.4/72.1 80.3/71.1 80.8/71.6 77.6/70.2 79.8/72.2 78.6/71.2
    下载: 导出CSV

    表  2  两种模型的检测效果

    Table  2.   Detection effect of the two models

    项目 眩光 阴影 无标线 交叉路口
    真实标签
    SCNN
    扩张卷积金字塔
    网络
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-06
  • 修回日期:  2019-02-21
  • 网络出版日期:  2019-03-07
  • 刊出日期:  2020-04-01

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