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基于改进YOLOv8的煤矿输送带异物检测

洪炎 汪磊 苏静明 汪瀚涛 李木石

洪炎,汪磊,苏静明,等. 基于改进YOLOv8的煤矿输送带异物检测[J]. 工矿自动化,2024,50(6):61-69.  doi: 10.13272/j.issn.1671-251x.2024050006
引用本文: 洪炎,汪磊,苏静明,等. 基于改进YOLOv8的煤矿输送带异物检测[J]. 工矿自动化,2024,50(6):61-69.  doi: 10.13272/j.issn.1671-251x.2024050006
HONG Yan, WANG Lei, SU Jingming, et al. Foreign object detection of coal mine conveyor belt based on improved YOLOv8[J]. Journal of Mine Automation,2024,50(6):61-69.  doi: 10.13272/j.issn.1671-251x.2024050006
Citation: HONG Yan, WANG Lei, SU Jingming, et al. Foreign object detection of coal mine conveyor belt based on improved YOLOv8[J]. Journal of Mine Automation,2024,50(6):61-69.  doi: 10.13272/j.issn.1671-251x.2024050006

基于改进YOLOv8的煤矿输送带异物检测

doi: 10.13272/j.issn.1671-251x.2024050006
基金项目: 国家重点研发计划项目(2021YFD2000204);国家自然科学基金项目(12304236,32301688,52174141);安徽数字农业工程技术研究中心开放项目(AHSZNYGCZXKF021);大学生创新创业基金项目(202210361053,202310361037);安徽理工大学研究生创新基金项目(2024cx2067)。
详细信息
    作者简介:

    洪炎(1979—),男,重庆万州人,教授,博士,主要研究方向为图像处理与物联网,E-mail:hong5212724@163.com

    通讯作者:

    汪磊(1997—),男,安徽六安人,硕士研究生,主要研究方向为深度学习与嵌入式系统、目标检测,E-mail:wljy2023@163.com

  • 中图分类号: TD634.1

Foreign object detection of coal mine conveyor belt based on improved YOLOv8

  • 摘要: 现有基于深度学习的输送带异物检测模型较大,难以在边缘设备部署,且对不同尺寸异物和小目标异物存在错检、漏检情况。针对上述问题,提出一种基于改进YOLOv8的煤矿输送带异物检测方法。采用深度可分离卷积、压缩和激励(SE)网络将YOLOv8主干网络中C2f模块的Bottleneck重新构建为DSBlock,在保持模型轻量化的同时提升检测性能;为增强对不同尺寸目标物体信息的获取能力,引入高效通道注意力(ECA) 机制,并对ECA的输入层进行自适应平均池化和自适应最大池化操作,得到跨通道交互MECA模块,以增强模块的全局视觉信息,进一步提升异物识别精度;将YOLOv8的3个检测头修改为4个轻量化小目标检测头,以增强对小目标的敏感性,有效降低小目标异物的漏检率和错检率。实验结果表明:改进YOLOv8的精确度达91.69%,mAP@50达92.27%,较YOLOv8分别提升了3.09%和4.07%;改进YOLOv8的检测速度达73.92帧/s,可充分满足煤矿输送带异物实时检测的需求;改进YOLOv8的精确度、mAP@50、参数量、权重大小和每秒浮点运算数均优于SSD,Faster-RCNN,YOLOv5,YOLOv7−tiny等主流目标检测算法。

     

  • 图  1  YOLOv8网络结构

    Figure  1.  YOLOv8 network structure

    图  2  CED−YOLO网络结构

    Figure  2.  CED-YOLO network structure

    图  3  数据增强流程

    Figure  3.  Data enhancement process

    图  4  SC2f模块结构

    Figure  4.  SC2f module structure

    图  5  DSBlock模块结构

    Figure  5.  DSBlock module structure

    图  6  MECA模块结构

    Figure  6.  MECA module structure

    图  7  改进后检测层

    Figure  7.  Improved detection layer

    图  8  改进后轻量化检测头

    Figure  8.  Improved lightweight detection head

    图  9  数据集图像

    Figure  9.  Dataset images

    图  10  模型性能指标

    Figure  10.  Model performance indicators

    图  11  不同模型的识别结果

    Figure  11.  Recognition results of different models

    图  12  模型改进前后热力图对比

    Figure  12.  Comparison of heat maps before and after model improvement

    表  1  实验硬件配置

    Table  1.   Experimental hardware configuration

    实验环境 配置
    操作系统 Windows 10
    CPU Intel(R) Core(TM)i5−13490F CPU@2.50 GHz
    GPU NVIDIA GeForce GTX 4060(8 G)
    深度学习框架 PyTorch 1.9.1+CUDA 11.1+CUDNN 8.0.5
    编译器 Python 3.8.18
    内存 32 GiB
    下载: 导出CSV

    表  2  消融实验结果

    Table  2.   Ablation experiment results

    序号 A B C D E 精确度/% 召回率/% mAP@50/% mAP@50∶95/% 参数量/
    106
    权重大小/
    MiB
    每秒浮点
    运算数/109
    速度/
    (帧·s−1
    1 × × × × × 88.60 80.19 88.20 56.70 3.00 6.3 8.1 162.55
    2 × × × × 89.25 83.09 89.36 58.62 3.00 6.3 8.1 163.23
    3 × × × 89.45 87.98 92.32 59.94 2.68 5.5 6.9 156.68
    4 × × 89.02 86.26 92.96 62.21 2.79 5.9 11.7 94.49
    5 × 92.03 84.30 91.92 60.89 2.79 5.9 11.7 111.26
    6 91.69 83.25 92.27 61.59 2.34 5.0 6.2 73.92
    下载: 导出CSV

    表  3  主流算法对比结果

    Table  3.   Comparison results of mainstream algorithms

    算法 精确度/% mAP@50/% 参数量/
    106
    权重大小/
    MiB
    每秒浮点
    运算数/109
    YOLOv3 87.54 89.06 12.12 24.4 18.9
    YOLOv5 89.38 88.52 2.50 5.3 7.1
    YOLOv7−tiny 84.40 89.70 6.01 12.3 13
    YOLOv8 88.60 88.20 3.00 6.3 8.1
    Faster−RCNN 66.13 55.09 136.73 108.0 401.7
    SSD 74.05 65.20 23.87 91.09 274.0
    文献[22]中算法 81.40 89.30 6.87 14.1 14.2
    文献[24]中算法 90.60 89.60 1.92 4.1 4.7
    CED−YOLO 91.69 92.27 2.34 5.0 6.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-05-06
  • 修回日期:  2024-05-25
  • 网络出版日期:  2024-07-10

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