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基于深度神经网络的综采工作面视频目标检测

杨艺 付泽峰 高有进 崔科飞 王科平

杨艺,付泽峰,高有进,等. 基于深度神经网络的综采工作面视频目标检测[J]. 工矿自动化,2022,48(8):33-42.  doi: 10.13272/j.issn.1671-251x.2022040003
引用本文: 杨艺,付泽峰,高有进,等. 基于深度神经网络的综采工作面视频目标检测[J]. 工矿自动化,2022,48(8):33-42.  doi: 10.13272/j.issn.1671-251x.2022040003
YANG Yi, FU Zefeng, GAO Youjin, et al. Video object detection of the fully mechanized working face based on deep neural network[J]. Journal of Mine Automation,2022,48(8):33-42.  doi: 10.13272/j.issn.1671-251x.2022040003
Citation: YANG Yi, FU Zefeng, GAO Youjin, et al. Video object detection of the fully mechanized working face based on deep neural network[J]. Journal of Mine Automation,2022,48(8):33-42.  doi: 10.13272/j.issn.1671-251x.2022040003

基于深度神经网络的综采工作面视频目标检测

doi: 10.13272/j.issn.1671-251x.2022040003
基金项目: 河南省科技攻关计划项目(212102210390);河南省煤矿智能开采技术创新中心支撑项目(2021YD01)。
详细信息
    作者简介:

    杨艺(1980-),男,湖北利川人,副教授,博士,主要研究方向为深度学习、强化学习和智能控制,E-mail:yangyi@hpu.edu.cn

    通讯作者:

    付泽峰(1995-),男,江西抚州人,硕士研究生,主要研究方向为信息处理与网络控制,E-mail:18864770547@163.com

  • 中图分类号: TD67

Video object detection of the fully mechanized working face based on deep neural network

  • 摘要: 综采工作面环境较复杂,地形狭长,多目标多设备经常出现在同一场景当中,使得目标检测难度加大。目前应用于煤矿井下的目标检测方法存在特征提取难度较大、泛化能力较差、检测目标类别较为单一等问题,且主要应用于巷道、井底车场等较为空旷场景,较少应用于综采工作面场景。针对上述问题,提出了一种基于深度神经网络的综采工作面视频目标检测方法。首先,针对综采工作面环境复杂多变、光照不均、煤尘大等不利条件,针对性挑选包含各角度、各环境条件下的综采工作面关键设备和人员的监控视频,并进行剪辑、删选,制作尽可能涵盖工作面现场各类场景的目标检测数据集。然后,通过对 YOLOv4模型进行轻量化改进,构建了LiYOLO目标检测模型。该模型利用CSPDarknet、SPP、PANet等加强特征提取模块对视频特征进行充分提取,使用6分类YoloHead进行目标检测,对综采工作面环境动态变化、煤尘干扰等具有较好的鲁棒性。最后,将LiYOLO目标检测模型部署到综采工作面,应用Gstreamer对视频流进行管理,同时使用TensorRT对模型进行推理加速,实现了多路视频流的实时检测。与YOLOv3、YOLOv4模型相比,LiYOLO目标检测模型具有良好的检测能力,能够满足综采工作面视频目标检测的实时性和精度要求,在综采工作面数据集上的平均准确率均值为96.48%,召回率为95%,同时视频检测帧率达67帧/s。工程应用效果表明,LiYOLO目标检测模型可同时检测、展示6路视频,且对于不同场景下的检测目标都有较好的检测效果。

     

  • 图  1  综采工作面视频目标检测流程

    Figure  1.  Flow of video object detection in fully mechanized working face

    图  2  不同条件下的综采工作面图像

    Figure  2.  Images of fully mechanized working face under different conditions

    图  3  数据集标注示例

    Figure  3.  Example of dataset annotation

    图  4  LiYOLO模型结构

    Figure  4.  LiYOLO model structure

    图  5  改进前后 的YoloHead

    Figure  5.  YoloHead before and after improved

    图  6  YOLOv4模型的mAP和损失变化曲线

    Figure  6.  mAP and loss variation curves of YOLOv4 model

    图  7  LiYOLO模型的mAP和损失变化曲线

    Figure  7.  mAP and loss variation curves of LiYOLO model

    图  8  3种模型对不同场景下设备及行人的检测效果

    Figure  8.  Detection effect of three models for devices and pedestrians in different scenes

    图  9  LiYOLO模型工程部署过程

    Figure  9.  Project deployment process of LiYOLO model

    图  10  多路视频检测效果

    Figure  10.  Multi-video detection effect

    表  1  不同条件下的图像采集数量

    Table  1.   Number of image samples under different conditions

    位置环境图像采集数量/张
    无尘轻微严重
    端头顺光4 5242 2632 263
    逆光1 132565565
    中部顺光22 62211 31311 313
    逆光5 6572 8282 828
    下载: 导出CSV

    表  2  标签分类

    Table  2.   Classification of labels

    序号标签名称序号标签名称
    1Groove(线槽) 4Roller(滚筒)
    2Conveyer(刮板输送机)5Person(人)
    3Shearer(采煤机)6face_guard(护帮板)
    下载: 导出CSV

    表  3  主要实验结果对比

    Table  3.   Comparison of main experimental results %

    模型mAPRecall
    YOLOv481.6990
    LiYOLO96.4895
    下载: 导出CSV

    表  4  检测时间

    Table  4.   Detection time

    模型检测时间/ms传输帧率/(帧·s−1
    YOLOv329.927.9
    YOLOv416.259.1
    LiYOLO16.166.8
    下载: 导出CSV

    表  5  未加速与加速后模型FPS对比

    Table  5.   Comparison of FPS between the unaccelerated model and the accelerated model 帧/s

    未加速FPS加速后FPS
    1路1路4路6路
    55.285.420.8×413.9×6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-01
  • 修回日期:  2022-08-09
  • 网络出版日期:  2022-08-09

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