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基于改进YOLOv3算法的公路车道线检测方法

崔文靓 王玉静 康守强 谢金宝 王庆岩 MIKULOVICHVladimir Ivanovich

崔文靓, 王玉静, 康守强, 谢金宝, 王庆岩, MIKULOVICH Vladimir Ivanovich. 基于改进YOLOv3算法的公路车道线检测方法. 自动化学报, 2022, 48(6): 1560−1568 doi: 10.16383/j.aas.c190178
引用本文: 崔文靓, 王玉静, 康守强, 谢金宝, 王庆岩, MIKULOVICH Vladimir Ivanovich. 基于改进YOLOv3算法的公路车道线检测方法. 自动化学报, 2022, 48(6): 1560−1568 doi: 10.16383/j.aas.c190178
Cui Wen-Liang, Wang Yu-Jing, Kang Shou-Qiang, Xie Jin-Bao, Wang Qing-Yan, Mikulovich Vladimir Ivanovich. Road lane line detection method based on improved YOLOv3 algorithm. Acta Automatica Sinica, 2022, 48(6): 1560−1568 doi: 10.16383/j.aas.c190178
Citation: Cui Wen-Liang, Wang Yu-Jing, Kang Shou-Qiang, Xie Jin-Bao, Wang Qing-Yan, Mikulovich Vladimir Ivanovich. Road lane line detection method based on improved YOLOv3 algorithm. Acta Automatica Sinica, 2022, 48(6): 1560−1568 doi: 10.16383/j.aas.c190178

基于改进YOLOv3算法的公路车道线检测方法

doi: 10.16383/j.aas.c190178
基金项目: 黑龙江省自然科学基金(LH2019E058), 黑龙江省本科高校青年创新人才培养计划(UNPYSCT-2017091), 黑龙江省普通高校基本科研业务专项基金资助项目(LGYC2018JC022)资助
详细信息
    作者简介:

    崔文靓:哈尔滨理工大学电气与电子工程学院硕士研究生. 主要研究方向为目标检测与计算机视觉. E-mail: cuiwliang@163.com

    王玉静:哈尔滨理工大学电气与电子工程学院副教授. 2015年获哈尔滨工业大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断, 状态评估与预测技术, 模式识别. E-mail: mirrorwyj@163.com

    康守强:哈尔滨理工大学电气与电子工程学院教授. 2011年获得白俄罗斯国立大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断, 状态评估与预测技术, 模式识别. E-mail: kangshouqiang@163.com

    谢金宝:哈尔滨理工大学电气与电子工程学院副教授. 2012年获得白俄罗斯国立大学博士学位. 主要研究方向为计算机视觉和自然语言处理. E-mail: xjbpost@163.com

    王庆岩:哈尔滨理工大学电气与电子工程学院讲师. 2018年获得哈尔滨工业大学工学博士学位. 主要研究方向为图像处理与模式识别, 遥感图像处理. 本文通信作者.E-mail: wangqy@hrbust.edu.cn

    MIKULOVICHVladimir Ivanovich:白俄罗斯国立大学教授. 1975年获白俄罗斯国立大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断, 状态评估与预测技术, 模式识别. E-mail: falcon@tut.by

Road Lane Line Detection Method Based on Improved YOLOv3 Algorithm

Funds: Supported by Natural Science Foundation of Heilongjiang Province (LH2019E058), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2017091), and Fundamental Research Foundation for Universities of Heilongjiang Province (LGYC2018JC022)
More Information
    Author Bio:

    CUI Wen-Liang Master student at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. His research interest covers target detection and computer vision

    WANG Yu-Jing Associate professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. She received her Ph.D. degree from Harbin Institute of Technology in 2015. Her research interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and pattern recognition

    KANG Shou-Qiang Professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. He received his Ph.D. degree from Belarusian State University, Minsk, Belarus in 2011. His research interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and pattern recognition

    XIE Jin-Bao Associate professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. He received his Ph.D. degree from Belarusian State University, Minsk, Belarus in 2012. His research interest covers computer vision and natural language processing

    WANG Qing-Yan Lecturer at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. He received his Ph.D. degree from Harbin Institute of Technology in 2018. His research interest covers image processing and pattern recognition, and remote sensing image processing. Corresponding author of this paper

    MIKULOVICH Vladimir Ivanovich Professor of Belarusian State University, Minsk, Belarus. He received his Ph.D. degree from Belarusian State University, Minsk, Belarus in 1975. His research interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and pattern recognition

  • 摘要: 针对YOLOv3算法在检测公路车道线时存在准确率低和漏检概率高的问题, 提出一种改进YOLOv3网络结构的公路车道线检测方法.该方法首先将图像划分为多个网格, 利用K-means++聚类算法, 根据公路车道线宽高固有特点, 确定目标先验框数量和对应宽高值; 其次根据聚类结果优化网络Anchor参数, 使训练网络在车道线检测方面具有一定的针对性; 最后将经过Darknet-53网络提取的特征进行拼接, 改进YOLOv3算法卷积层结构, 使用GPU进行多尺度训练得到最优的权重模型, 从而对图像中的车道线目标进行检测,并选取置信度最高的边界框进行标记.使用Caltech Lanes数据库中的图像信息进行对比试验, 实验结果表明, 改进的YOLOv3算法在公路车道线检测中平均准确率(Mean average precision, mAP)为95%, 检测速度可达50帧/s, 较YOLOv3原始算法mAP值提升了11%, 且明显高于其他车道线检测方法.
  • 图  1  边界框参数归一化处理

    Fig.  1  The normalization of boundary box parameters

    图  2  Darknet-53网络结构

    Fig.  2  The network structure of Darknet-53

    图  3  改进YOLOv3算法的网络结构

    Fig.  3  The network structure of the improved YOLOv3 algorithm

    图  4  公路车道线检测框图

    Fig.  4  The flow chart of road lane line detection

    图  5  不同$k$值对应的目标函数

    Fig.  5  The objective function corresponding to different $k$ values

    图  6  平均损失变化曲线

    Fig.  6  The change curve of average loss

    图  7  平均交并比变化曲线

    Fig.  7  The change curve of average IOU

    图  8  车道线测试效果

    Fig.  8  The result of lane line test

    图  9  测试集图像在不同网络结构中的检测准确率

    Fig.  9  The detection accuracy of test images in different network structures

    表  1  不同$k$值对应的先验框宽高

    Table  1  The width and height of priori boxes corresponding to different$k$values

    $k$ = 7 $k$ = 8 $k$ = 9 $k$ = 10 $k$ = 11
    (6, 9) (6, 9) (6, 9) (5, 12) (5, 7)
    (10, 15) (8, 12) (9, 14) (5, 17) (7, 11)
    (13, 21) (11, 17) (12, 18) (7, 11) (10, 14)
    (19, 30) (15, 24) (15, 24) (10, 14) (10, 18)
    (27, 44) (20, 32) (20, 32) (11, 18) (13, 20)
    (36, 60) (26, 43) (26, 43) (15, 24) (16, 25)
    (141, 10) (36, 69) (32, 51) (20, 32) (21, 32)
    (141, 10) (40, 69) (27, 44) (26, 43)
    (141, 10) (36, 60) (32, 51)
    (141, 10) (40, 70)
    (141, 10)
    下载: 导出CSV

    表  2  不同网络结构测试性能对比

    Table  2  The test performance comparison of different network structures

    网络 平均测试时间 (s) 平均漏检率 (%) mAP (%)
    Caltech[12] 72.3
    VPGNet[25] 88.4
    YOLOv3-107 0.021 8.9 84.4
    YOLOv3-101 0.019 0 89.8
    YOLOv3-K-107 0.021 2.2 91.4
    YOLOv3-K-101 0.019 0 95.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-21
  • 录用日期:  2019-05-23
  • 网络出版日期:  2022-03-27
  • 刊出日期:  2022-06-02

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