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基于Darknet框架下YOLO v2算法的车辆多目标检测方法

李珣 刘瑶 李鹏飞 张蕾 赵征凡

李珣, 刘瑶, 李鹏飞, 张蕾, 赵征凡. 基于Darknet框架下YOLO v2算法的车辆多目标检测方法[J]. 交通运输工程学报, 2018, 18(6): 142-158. doi: 10.19818/j.cnki.1671-1637.2018.06.015
引用本文: 李珣, 刘瑶, 李鹏飞, 张蕾, 赵征凡. 基于Darknet框架下YOLO v2算法的车辆多目标检测方法[J]. 交通运输工程学报, 2018, 18(6): 142-158. doi: 10.19818/j.cnki.1671-1637.2018.06.015
LI Xun, LIU Yao, LI Peng-fei, ZHANG Lei, ZHAO Zheng-fan. Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 142-158. doi: 10.19818/j.cnki.1671-1637.2018.06.015
Citation: LI Xun, LIU Yao, LI Peng-fei, ZHANG Lei, ZHAO Zheng-fan. Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 142-158. doi: 10.19818/j.cnki.1671-1637.2018.06.015

基于Darknet框架下YOLO v2算法的车辆多目标检测方法

doi: 10.19818/j.cnki.1671-1637.2018.06.015
基金项目: 

国家自然科学基金项目 51607133

陕西省自然科学基础研究计划项目 2016JQ5106

陕西省教育厅专项科研计划项目 16JK1342

详细信息
    作者简介:

    李珣(1981-), 男, 陕西子长人, 西安工程大学副教授, 工学博士, 从事微观交通流模型与交通图像处理方法研究

    通讯作者:

    刘瑶(1994-), 女, 陕西延安人, 西安工程大学工学硕士研究生

  • 中图分类号: U492.84

Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework

More Information
  • 摘要: 针对道路车辆目标检测传统方法需随场景变化提取不同特征, 检测率较低与鲁棒性差的问题, 提出了一种基于Darknet框架下YOLO v2算法的车辆多目标检测方法; 根据目标路段场景与车流量的变化对YOLO-voc网络模型进行改进, 基于ImageNet数据集和微调技术获得分类训练网络模型, 对训练结果和车辆目标特征进行分析后进一步调整改进的算法参数, 最终获得更适合于道路车辆检测的YOLO-vocRV网络模型下车辆多目标检测方法; 为验证检测方法的有效性和完备性, 采用不同车流密度进行了车辆多目标检测试验, 并与经典YOLO-voc、YOLO9000模型进行了对比; 采用改进YOLO-vocRV网络模型, 选取20 000次迭代, 分析了多目标检测结果。试验结果表明: 在阻塞流样本条件下, YOLO9000网络模型检测率为93.71%, YOLO-voc网络模型检测率为94.48%, 改进YOLO-vocRV网络模型检测率达到了96.95%, 因此, 改进网络模型YOLOvocRV检测率较高; YOLO-vocRV模型精确度和召回率均聚集在0.95, 因此, 在获得较好精确度的条件下损失的召回率明显较小, 达到了很好的折中; 采用混合样本训练后, 基于YOLO-vocRV模型的车辆多目标检测方法的检测率在自由流状态下可达99.11%, 同步流状态下可达97.62%, 阻塞流状态下可达到97.14%, 具有较小的误检率和良好的鲁棒性。

     

  • 图  1  YOLO v2中CNN网络结构

    Figure  1.  CNN network structure in YOLO v2

    图  2  YOLO v2目标检测方法

    Figure  2.  YOLO v2target detection method

    图  3  试验模型测试结果

    Figure  3.  Test results of test models

    图  4  YOLO-vocRV网络结构

    Figure  4.  YOLO-vocRV network structure

    图  5  车辆检测流程

    Figure  5.  Process of vehicle detection

    图  6  自由流数据样本

    Figure  6.  Data samples of free flow

    图  7  同步流数据样本

    Figure  7.  Data samples of synchronous flow

    图  8  阻塞流数据样本

    Figure  8.  Data samples of blocking flow

    图  9  同步流条件下损失函数对数曲线

    Figure  9.  Loss function logarithmic curves under synchronous flow

    图  10  自由流条件下不同模型验证结果

    Figure  10.  Verification results of different models under free flow condition

    图  11  阻塞流条件下不同模型验证结果

    Figure  11.  Verification results of different models under blocking flow condition

    图  12  自由流训练样本下YOLO9000模型测试结果

    Figure  12.  Test results of YOLO9000model under free flow training samples

    图  13  自由流训练样本下YOLO-voc模型测试结果

    Figure  13.  Test results of YOLO-voc model under free flow training samples

    图  14  自由流训练样本下YOLO-vocRV模型测试结果

    Figure  14.  Test results of YOLO-vocRV model under free flow training samples

    图  15  同步流训练样本下YOLO9000模型测试结果

    Figure  15.  Test results of YOLO9000model under synchronous flow training samples

    图  16  同步流训练样本下YOLO-voc模型测试结果

    Figure  16.  Test results of YOLO-voc model under synchronous flow training samples

    图  17  同步流训练样本下YOLO-vocRV模型测试结果

    Figure  17.  Test results of YOLO-vocRV model under synchronous flow training samples

    图  18  阻塞流下训练20 000次YOLO9000模型的测试结果

    Figure  18.  Test results of YOLO9000model under block flow and training iteration 20 000times

    图  19  阻塞流下训练20 000次YOLO-voc模型的测试结果

    Figure  19.  Test results of YOLO-voc model under block flow and training iteration 20 000times

    图  20  阻塞流下训练20 000次YOLO-vocRV模型的测试结果

    Figure  20.  Test results of YOLO-vocRV model under block flow and training iteration 20 000times

    表  1  网络框架

    Table  1.   Network framework

    下载: 导出CSV

    表  2  同步流下不同模型训练样本结果

    Table  2.   Results of different models under synchronous flow training samples

    下载: 导出CSV

    表  3  阻塞流下不同模型训练样本结果

    Table  3.   Results of different models under block flow training samples

    下载: 导出CSV

    表  4  混合训练样本下YOLO-vocRV模型测试结果

    Table  4.   Test results of YOLO-vocRV model under mixed training samples

    下载: 导出CSV
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  • 收稿日期:  2018-06-26
  • 刊出日期:  2018-12-25

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