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基于双注意力机制的车道线检测

任凤雷 周海波 杨璐 何昕

任凤雷, 周海波, 杨璐, 何昕. 基于双注意力机制的车道线检测[J]. 中国光学(中英文). doi: 10.37188/CO.2022-0033
引用本文: 任凤雷, 周海波, 杨璐, 何昕. 基于双注意力机制的车道线检测[J]. 中国光学(中英文). doi: 10.37188/CO.2022-0033
REN Feng-lei, ZHOU Hai-bo, YANG Lu, HE Xin. Lane detection based on dual attention mechanism[J]. Chinese Optics. doi: 10.37188/CO.2022-0033
Citation: REN Feng-lei, ZHOU Hai-bo, YANG Lu, HE Xin. Lane detection based on dual attention mechanism[J]. Chinese Optics. doi: 10.37188/CO.2022-0033

基于双注意力机制的车道线检测

doi: 10.37188/CO.2022-0033
基金项目: 天津市自然科学基金重点项目(No. 17JCZDJC30400);广东省重点领域研发计划项目(No. 2019B090922002)
详细信息
    作者简介:

    任凤雷(1991—),男,河北沧州人,工学博士,讲师,于2015年本科毕业于吉林大学,2020年博士毕业于中国科学院长春光学精密机械与物理研究所,主要从事数字图像处理,自动驾驶视觉环境感知方面的研究。E-mail:renfenglei15@mails.ucas.edu.cnrenfenglei15@mails.ucas.edu.cn

    周海波(1973—),男,黑龙江肇东人,博士,教授,博士生导师,1998年与佳木斯大学获得学士学位,2005年于佳木斯大学获得硕士学位,2009年于吉林大学获得博士学位,主要从事计算机视觉、人工智能、智能机器人技术等方面的研究。E-mail:haibo_zhou@163.com

    杨璐(1982-),女,天津人,博士,副教授,硕士生导师,2005年于哈尔滨工业大学获得学士学位,2007年于吉林大学获得硕士学位,2011年于吉林大学获得博士学位,主要从事计算机视觉、机器学习等方面的研究。E-mail: yanglu8206@163.com

    何昕(1966-),男,吉林省长春人,研究员,博士研究生导师,1988年毕业于哈尔滨工业大学获得学士学位, 1991年于长春光机所获得硕士学位,主要从事图像处理、光电测量等方面的研究。 E-mail: hexin6627@sohu.com

  • 中图分类号: TP394.1

Lane detection based on dual attention mechanism

Funds: Supported by Key projects of Tianjin Natural Science Foundation (No. 17JCZDJC30400); Special Project for Research and Development in Key Areas of Guangdong Province (No. 2019B090922002)
More Information
  • 摘要: 为了提升车道线检测算法在障碍物遮挡等复杂情况下的检测性能,本文提出了一种基于双注意力机制的多车道线检测算法。首先,本文通过设计基于空间和通道双注意力机制的车道线语义分割网络,得到分别代表车道线像素和背景区域的二值分割结果;然后,引入HNet网络结构,使用其输出的透视变换矩阵将分割图转换到鸟瞰视图,继而进行曲线拟合并逆变换回原图像空间实现多车道线的检测;最后,将图像中线两侧车道线所包围的区域定义为目前行驶的行车车道。本文算法在Tusimple数据集凭借134FPS的实时性表现达到了96.63%的准确率,在CULane数据集取得了77.32%的精确率。实验结果表明,本文算法可以针对包括障碍物遮挡等不同场景下的多条车道线及行车车道进行实时检测,其性能相比较现有算法得到了显著的提升。

     

  • 图 1  车道线检测示意图

    Figure 1.  Diagram of lane detection

    图 2  图像语义分割示意图

    Figure 2.  Diagram of semantic segmentation

    图 3  本文车道线检测算法示意图

    Figure 3.  Diagram of our lane detection algorithm

    图 4  扩张卷积示意图

    Figure 4.  Diagram of Atrous Convolution

    图 5  空间注意力机制示意图

    Figure 5.  Diagram of the Position Attention Module

    图 6  通道注意力机制示意图

    Figure 6.  Diagram of the Channel Attention Module

    图 7  行车车道确认示意图

    Figure 7.  Diagram of lane identification

    图 8  本文算法Tusimple数据集车道线检测结果

    Figure 8.  Lane detection results of our algorithm on Tusimple

    图 9  本文算法CULane数据集车道线检测结果

    Figure 9.  Lane detection results of our algorithm on CULane

    表  1  Tusimple数据集定量实验结果

    Table  1.   Result of the quantitative experiment on Tusimple

    Methodacc(%)FP(%)FN(%)FPS
    SCNN[18]96.536.171.807.5
    LaneNet[13]96.387.802.4452.6
    PolylaneNet[19]93.369.429.33115
    FastDraw[20]95.207.604.5090.3
    R-50-E2E[21]96.043.114.09
    Ours96.636.022.03134
    下载: 导出CSV

    表  2  CULane数据集定量实验结果

    Table  2.   Result of the quantitative experiment on CULane

    MethodNormalCrowdDazzleShadowNoline
    SCNN[18]90.6069.7058.5066.9043.40
    FastDraw[20]85.9063.6057.0069.9040.60
    UFSD-18[1]87.7066.0058.4062.8040.20
    UFSD-34[1]90.7070.2059.5069.3044.40
    LaneATT[22]91.1772.7165.8268.0349.13
    Ours91.2176.3369.5173.2550.16
    MethodArrowCurveCrossNightTotal
    SCNN[18]84.1064.40199066.1071.60
    FastDraw[20]79.4065.20701357.80-
    UFSD-18[1]81.0057.90174362.1068.40
    UFSD-34[1]85.7069.50203766.7072.30
    LaneATT[22]87.8263.75102068.5875.13
    Ours88.7271.25126570.7377.32
    下载: 导出CSV
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  • 网络出版日期:  2022-06-16

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