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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,等.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(06):142-158.
引用本文: 李 珣,刘 瑶,李鹏飞,等.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(06):142-158.
LI Xun,LIU Yao,LI Peng-fei,et al.Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework[J].Journal of Traffic and Transportation Engineering,2018,18(06):142-158.
Citation: LI Xun,LIU Yao,LI Peng-fei,et al.Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework[J].Journal of Traffic and Transportation Engineering,2018,18(06):142-158.

基于Darknet框架下YOLO v2算法的车辆多目标检测方法

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

  • 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YOLO-vocRV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。

     

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出版历程
  • 收稿日期:  2018-06-26
  • 刊出日期:  2018-12-27

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