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井下无人驾驶电机车行驶场景中多目标检测研究

郭永存 童佳乐 王爽

郭永存,童佳乐,王爽. 井下无人驾驶电机车行驶场景中多目标检测研究[J]. 工矿自动化,2022,48(6):56-63.  doi: 10.13272/j.issn.1671-251x.2022030001
引用本文: 郭永存,童佳乐,王爽. 井下无人驾驶电机车行驶场景中多目标检测研究[J]. 工矿自动化,2022,48(6):56-63.  doi: 10.13272/j.issn.1671-251x.2022030001
GUO Yongcun, TONG Jiale, WANG Shuang. Research on multi-object detection in driving scene of underground unmanned electric locomotive[J]. Journal of Mine Automation,2022,48(6):56-63.  doi: 10.13272/j.issn.1671-251x.2022030001
Citation: GUO Yongcun, TONG Jiale, WANG Shuang. Research on multi-object detection in driving scene of underground unmanned electric locomotive[J]. Journal of Mine Automation,2022,48(6):56-63.  doi: 10.13272/j.issn.1671-251x.2022030001

井下无人驾驶电机车行驶场景中多目标检测研究

doi: 10.13272/j.issn.1671-251x.2022030001
基金项目: 国家自然科学基金资助项目(51904007);安徽省科技重大专项资助项目(202003a05020021);安徽高校协同创新资助项目(GXXT-2020-60)。
详细信息
    作者简介:

    郭永存(1965—),男,安徽舒城人,教授,博士研究生导师,研究方向为矿山智能化装备与技术,E-mail:guoyc1965@126.com

    通讯作者:

    童佳乐(1997—),男,安徽阜阳人,硕士研究生,研究方向为矿山智能化装备与技术,E-mail:tongjl1023@163.com

  • 中图分类号: TD64

Research on multi-object detection in driving scene of underground unmanned electric locomotive

  • 摘要: 目前煤矿井下无人驾驶有轨电机车在行驶过程中,对轨道中的石块及其他小型障碍物的识别存在检测速度慢、检测精度低,且对于重叠目标,易造成漏检、错检等问题。针对上述问题,提出了一种井下电机车多目标检测模型−SE−HDC−Mask R−CNN模型。该模型基于Mask R−CNN进行改进,通过在主干特征提取网络ResNet的残差块中嵌入压缩−激励(SE)模块,学习各个通道的重要程度和相互联系,增强网络对特征的选择和捕获能力;将残差块中卷积核大小为3×3的标准卷积替换成混合空洞卷积(HDC),在不改变特征图大小、不增加参数计算量的前提下,通过增加卷积核处理数据时各值之间的距离达到增大感受野的目的。实验结果表明:SE−HDC−Mask R−CNN模型可有效提取轨道、电机车、信号灯、行人和石块目标,在井下电机车多场景运行数据集上的平均准确率均值为95.4%,平均掩码分割精度为88.1%,平均边界框交并比为91.7%,相较于Mask R−CNN模型均提升了0.5%,对信号灯、石块(小目标)的检测精度分别提升了0.7%和4.1%;SE−HDC−Mask R−CNN模型的综合性能优于YOLOV2,YOLOV3−Tiny,SSD,Faster R−CNN等模型,可有效解决小目标漏检问题;SE−HDC−Mask R−CNN模型在煤巷直轨、弯轨、黑暗环境、多目标重叠等场景下均可有效实现目标检测,具有一定泛化能力及较高鲁棒性,基本满足无人驾驶电机车障碍物检测需求。

     

  • 图  1  Mask R−CNN模型架构

    Figure  1.  Architecture of Mask R-CNN model

    图  2  优化后的Conv block结构

    Figure  2.  Structure of optimized Conv block

    图  3  改进ResNet网络结构

    Figure  3.  Structure of improved ResNet network

    图  4  井下无人驾驶电机车多目标检测技术构架

    Figure  4.  Multi-object detection technology framework for underground unmanned electric locomotive

    图  5  掩码分割质量评价

    Figure  5.  Evaluation of mask segmentation quality

    图  6  ResNet50/101网络下的模型损失

    Figure  6.  Model loss under ResNet50/101 network

    图  7  井下电机车行驶场景中不同网络模型的目标检测及分割结果

    Figure  7.  Object detection and segmentation results of different network models in underground electric locomotive driving scene

    图  8  SE−HDC−Mask R−CNN模型在不同场景下的目标检测结果

    Figure  8.  Object detection results of SE-HDC-Mask R-CNN model in different scenarios

    表  1  井下无人驾驶电机车多目标检测实验硬件参数

    Table  1.   Experimental hardware parameters of multi-object detection of underground unmanned electric locomotive

    硬件参数
    系统Ubuntu18.04
    CPU英特尔 Core i7−8700 @3.02 GHz 六核
    GPUNvidia GeForce GTX1080(8 GB)
    内存16 GB(威士奇DDR3 1 600 MHz)
    下载: 导出CSV

    表  2  ResNet50/101网络下的定性分析

    Table  2.   Qualitative analysis under ResNet50/101 network

    主干特征提取网络mAP/%mIoUmask/%mIoUbox/%帧率/(帧·s−1
    ResNet5094.9287.6291.204.92
    ResNet10196.1088.3091.403.83
    下载: 导出CSV

    表  3  SE−HDC−Mask R−CNN模型与Mask R−CNN50模型对比结果

    Table  3.   Comparison results between SE-HDC-Mask R-CNN model and Mask R-CNN50 model %

    目标APIoUmaskIoUbox
    Mask
    R−CNN50
    SE−HDC−Mask
    R−CNN50
    Mask
    R−CNN50
    SE−HDC−Mask
    R−CNN50
    Mask
    R−CNN50
    SE−HDC−Mask
    R−CNN50
    轨道96.395.587.987.891.891.5
    电机车99.199.192.692.793.893.8
    信号灯88.989.685.585.190.390.6
    行人99.297.788.688.292.692.6
    石块91.195.283.586.587.589.9
    下载: 导出CSV

    表  4  不同网络模型的评价结果

    Table  4.   Evaluation results of different network models  %

    模型mAPmIoUmaskmIoUbox
    YOLOV283.676.0
    YOLOV3-Tiny92.981.5
    SSD85.681.1
    Faster R-CNN85.488.5
    Mask R-CNN5094.987.691.2
    SE-HDC-Mask R-CNN5095.488.191.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-01
  • 修回日期:  2022-06-10
  • 网络出版日期:  2022-04-06

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