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基于改进YOLOv7的煤矿带式输送机异物识别

毛清华 李世坤 胡鑫 薛旭升 姚丽杰

毛清华,李世坤,胡鑫,等. 基于改进YOLOv7的煤矿带式输送机异物识别[J]. 工矿自动化,2022,48(12):26-32.  doi: 10.13272/j.issn.1671-251x.2022100011
引用本文: 毛清华,李世坤,胡鑫,等. 基于改进YOLOv7的煤矿带式输送机异物识别[J]. 工矿自动化,2022,48(12):26-32.  doi: 10.13272/j.issn.1671-251x.2022100011
MAO Qinghua, LI Shikun, HU Xin, et al. Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7[J]. Journal of Mine Automation,2022,48(12):26-32.  doi: 10.13272/j.issn.1671-251x.2022100011
Citation: MAO Qinghua, LI Shikun, HU Xin, et al. Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7[J]. Journal of Mine Automation,2022,48(12):26-32.  doi: 10.13272/j.issn.1671-251x.2022100011

基于改进YOLOv7的煤矿带式输送机异物识别

doi: 10.13272/j.issn.1671-251x.2022100011
基金项目: 陕西省重点研发计划项目(2018ZDCXL-GY-06-04);煤矿机电系统智能测控创新团队项目(2022TD-043)。
详细信息
    作者简介:

    毛清华(1984—),男,江西吉安人,教授,博士,主要研究方向为煤矿机电设备智能检测与控制、机器人、机械传动系统故障诊断和图像智能识别等,E-mail:maoqh@xust.edu.cn

  • 中图分类号: TD528/634

Foreign object recognition of belt conveyor in coal mine based on improved YOLOv7

  • 摘要: 带式输送机煤流中会掺杂锚杆、角铁、木条、矸石、大块煤等异物,易导致输送带撕裂、转接处堵塞甚至断带。针对带式输送机巡检机器人难以在井下光照不均及带式输送机高速运行环境中高效、准确识别异物及模型部署不便等问题,以及YOLOv7模型对目标特征提取能力高,但识别速度较慢的特点,提出了一种基于改进YOLOv7的煤矿带式输送机异物识别方法。运用限制对比度自适应直方图均衡化方法对采集的带式输送机监控图像进行增强,提高图像中物体轮廓的清晰度;对YOLOv7模型进行改进,通过在主干提取网络引入轻量化无参注意力机制,提高模型对图像复杂背景的抗干扰能力和对异物特征的提取能力,同时引入深度可分离卷积代替主干特征提取网络中的普通卷积,提高异物识别速度;使用TensorRT引擎将训练后的改进YOLOv7模型进行转换并部署在NVIDIA Jetson Xavier NX上,实现了模型的加速。对煤矿井下分辨率为1 920×1 080的带式输送机监控视频进行识别,实验结果表明:改进YOLOv7模型的识别效果优于YOLOv5L和YOLOv7模型,识别精确率达92.8%,识别速度为25.64帧/s,满足精确、高效识别带式输送机异物的要求。

     

  • 图  1  改进YOLOv7结构

    Figure  1.  Structure of improved YOLOv7

    图  2  SimAM原理

    Figure  2.  Principle of simple and parameter-free attention module

    图  3  TensorRT使用流程

    Figure  3.  TensorRT usage process

    图  4  带式输送机异物识别流程

    Figure  4.  Belt conveyor foreign object recognition process

    图  5  图像增强前后对比

    Figure  5.  Comparison of the images before and after image enhancement

    图  6  不同模型识别结果

    Figure  6.  Recognition results of different models

    表  1  图像清晰度评价结果

    Table  1.   Evaluation results of image definition

    图像EntropyBrenner
    原图5.113 31.909
    增强后图像5.289 94.094
    下载: 导出CSV

    表  2  不同模型的平均精确率、平均召回率和识别时间

    Table  2.   Average precision, average recall and recognition time of different models

    模型平均精确率/%平均召回率/%识别时间/s
    YOLOv5L90.985.00.047
    YOLOv789.484.50.027
    改进YOLOv793.187.40.025
    下载: 导出CSV

    表  3  YOLOv7改进前后异物识别精确率和召回率对比

    Table  3.   Comparison of foreign object recognition precision and recall before and after YOLOv7 improvement

    类别YOLOv7改进YOLOv7
    精确率/%召回率/%精确率/%召回率/%
    锚杆90.685.193.488.5
    角铁89.290.190.693.1
    木条91.384.797.786.1
    矸石87.484.988.989.3
    大块煤88.577.894.980.0
    下载: 导出CSV

    表  4  消融实验结果

    Table  4.   Ablation experimental results

    增强SimAMDWConv平均精确率/%识别时间/s
    89.40.027
    90.30.027
    94.00.030
    88.10.024
    93.10.025
    下载: 导出CSV

    表  5  不同模型平均精确率和识别时间

    Table  5.   Average precision and recognition time of different models

    模型平均精确率/%识别时间/s
    YOLOv5L90.00.049
    YOLOv788.90.041
    改进YOLOv792.80.039
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-08
  • 修回日期:  2022-12-18
  • 网络出版日期:  2022-12-05

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