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基于CenterNet−GhostNet的选煤厂危险区域人员检测

张翼翔 林松 李雪

张翼翔,林松,李雪. 基于CenterNet−GhostNet的选煤厂危险区域人员检测[J]. 工矿自动化,2022,48(4):66-71.  doi: 10.13272/j.issn.1671-251x.2021080058
引用本文: 张翼翔,林松,李雪. 基于CenterNet−GhostNet的选煤厂危险区域人员检测[J]. 工矿自动化,2022,48(4):66-71.  doi: 10.13272/j.issn.1671-251x.2021080058
ZHANG Yixiang, LIN Song, LI Xue. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Journal of Mine Automation,2022,48(4):66-71.  doi: 10.13272/j.issn.1671-251x.2021080058
Citation: ZHANG Yixiang, LIN Song, LI Xue. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Journal of Mine Automation,2022,48(4):66-71.  doi: 10.13272/j.issn.1671-251x.2021080058

基于CenterNet−GhostNet的选煤厂危险区域人员检测

doi: 10.13272/j.issn.1671-251x.2021080058
基金项目: 国家自然科学基金资助项目(61936008)。
详细信息
    作者简介:

    张翼翔(1996—),男,山东泰安人,硕士研究生,研究方向为深度学习,E-mail:2627421136@qq.com

  • 中图分类号: TD67

Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet

  • 摘要: 选煤厂危险区域人员全身目标检测因粉尘、雾气干扰难以准确与生产环境背景区分,而人员头部特征相对易于辨识,且人头在监控视角下被遮挡的可能性较低,因此危险区域人员检测使用人员头部检测代替人员全身目标检测。目前基于深度学习的轻量化目标检测模型在特征提取时信息损失多,对人头目标的检测能力有限。针对该问题,提出了轻量化人员检测模型CenterNet−GhostNet。该模型以CenterNet网络为基础框架,将轻量化网络GhostNet与特征金字塔相结合作为特征提取网络,GhostNet对输入图像进行特征提取,提升网络特征表达能力,特征金字塔对GhostNet提取的不同分辨率的特征图中包含的信息进行融合,在提取高层语义特征的同时保留较多的细节信息;在较高分辨率的单个输出特征图上使用3个相互独立的卷积操作分支进行解码计算,以充分利用特征图包含的细节信息。实验结果表明:CenterNet−GhostNet模型对佩戴安全帽和未佩戴安全帽两类人头目标的检测精度分别为93.7%和91.7%,均优于通用的轻量化模型SSD−MobileNet、YOLOv4 Tiny和CenterNet−Res18;CenterNet−GhostNet模型部署在NVIDIA Jetson Nano上的单帧检测耗时为67 ms,满足选煤厂危险区域人员高精度、实时检测要求。

     

  • 图  1  CenterNet−GhostNet模型框架

    Figure  1.  Framework of CenterNet-GhostNet model

    图  2  基于GhostNet和FPN的特征提取网络结构

    Figure  2.  Feature extraction network structure based on GhostNet and feature pyramid network

    图  3  G−bneck结构

    Figure  3.  Structure of G-bneck

    图  4  Ghost模块

    Figure  4.  Ghost module

    图  5  CenterNet−GhostNet模型检测结果

    Figure  5.  Detection results of CenterNet-GhostNet model

    图  6  CenterNet−Res18模型检测结果

    Figure  6.  Detection results of CenterNet-Res18 model

    图  7  选煤厂危险区域人员检测布置

    Figure  7.  Personnel detection arrangement in dangerous area of coal preparation plant

    表  1  GhostNet参数

    Table  1.   Parameters of GhostNet

    输入尺寸结构步长
    512×512×3Conv2d(3×3)2
    256×256×16G−bneck×21,2
    128×128×24G−bneck×21,2
    64×64×40G−bneck×21,2
    32×32×80G−bneck×41,1,1,1
    32×32×112G−bneck×21,2
    16×16×160G−bneck×41,1,1,1
    16×16×160Conv2d(3×3)1
    下载: 导出CSV

    表  2  不同模型检测精度和检测速度对比

    Table  2.   Comparison of detection accuracy and detection speed among different models

    模型mAP/%FPS/(帧·s−1
    佩戴安全帽未佩戴安全帽
    SSD−MobileNet87.183.3167
    YOLOv4 Tiny91.589.7117
    CenterNet−Res1888.685.1155
    CenterNet−GhostNet93.791.7146
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-21
  • 修回日期:  2022-04-02
  • 网络出版日期:  2022-03-05

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