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面向电力开关柜的轻量型GB-YOLOv5m状态检测方法

崔昊杨 杨可欣 葛海华 许永鹏 王浩然 杨程 戴莹莹

崔昊杨, 杨可欣, 葛海华, 许永鹏, 王浩然, 杨程, 戴莹莹. 面向电力开关柜的轻量型GB-YOLOv5m状态检测方法[J]. 电子与信息学报, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288
引用本文: 崔昊杨, 杨可欣, 葛海华, 许永鹏, 王浩然, 杨程, 戴莹莹. 面向电力开关柜的轻量型GB-YOLOv5m状态检测方法[J]. 电子与信息学报, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288
CUI Haoyang, YANG Kexin, GE Haihua, XU Yongpeng, WANG Haoran, YANG Cheng, DAI Yingying. Lightweight GB-YOLOv5m State Detection Method for Power Switchgear[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288
Citation: CUI Haoyang, YANG Kexin, GE Haihua, XU Yongpeng, WANG Haoran, YANG Cheng, DAI Yingying. Lightweight GB-YOLOv5m State Detection Method for Power Switchgear[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3777-3787. doi: 10.11999/JEIT220288

面向电力开关柜的轻量型GB-YOLOv5m状态检测方法

doi: 10.11999/JEIT220288
基金项目: 国家自然科学基金(52177185)
详细信息
    作者简介:

    崔昊杨:男,博士,教授,主要研究方向为电力设备状态检测

    杨可欣:女,硕士生,研究方向为电力设备图像处理

    葛海华:女,工程师,主要研究方向为蒸汽联合循环机组运行

    许永鹏:男,博士,助理研究员,主要研究方向为电力设备状态检测

    王浩然:男,硕士生,研究方向为光伏并网发电

    杨程:男,博士,讲师,主要研究方向为多媒体信号处理、机器学习

    通讯作者:

    崔昊杨 cuihy@shiep.edu.cn

  • 中图分类号: TM930.9; TN911.73

Lightweight GB-YOLOv5m State Detection Method for Power Switchgear

Funds: The National Natural Science Foundation of China (52177185)
  • 摘要: 电力开关柜状态灯及仪表具有布局高密、异位同像的特点,从而对边端图像处理技术中的目标形貌、色度对比等基础特征检测能力以及轻量识别能力提出更高要求,为此该文提出一种Ghost-BiFPN-YOLOv5m(GB-YOLOv5m)方法。采用加权双向特征金字塔(BiFPN)结构,赋予特征层不同权重以传递更多有效特征信息;增加一个检测层尺度,提升网络对于小目标的检测精度,解决状态灯高密布局引起的小目标识别难问题;利用Ghost-Bottleneck结构替换原主干网络的Bottleneck复杂结构,实现模型的轻量化,为在边端部署模型提供有利条件;通过图像增强技术对有限样本进行状态灯和仪表传递特征的扩充,并通过迁移学习实现算法高速收敛。经10 kV开关柜实测,结果表明该算法对柜体状态灯及仪表共16类目标识别准确率高,均值平均精度(mAP)达97.3%,fps为37.533帧;相较于YOLOv5m算法,在模型大小缩小了37.04%的基础上,mAP提升了10.2%,说明所提方法对灯体与表体的检测能力大幅提升,且轻量识别效率提升明显,对于开关柜电力状态的实时核验与数字孪生信息交互,具有一定的现实意义。
  • 图  1  YOLOv5m算法首次卷积所得部分特征图

    图  2  GB-YOLOv5m模型的网络结构示意图

    图  3  Ghost模块与常规卷积模块对比

    图  4  两个Ghost模块

    图  5  使用Ghost卷积后的特征图

    图  6  BiFPN特征融合单元

    图  7  优化方案与原YOLOv5m算法特征图对比

    图  8  实验场景及设备部署情况

    图  9  损失函数曲线对比图

    图  10  GB-YOLOv5m算法检测结果图

    图  11  不同角度下各对比算法所得检测结果图

    表  1  GB-YOLOv5m与其他算法对比结果

    序号算法P(%)R(%)mAP(%)Param(MB)fps
    1FasterR-CNN39.76044.69042.9125.5611.220
    2SSD30038.40537.63339.9999.7623.270
    3YOLOv4-Tiny43.60037.80043.605.9133.707
    4YOLOX82.32483.03384.4934.2114.633
    5DETR-DC528.62235.41129.1860.226.970
    6YOLOv5m88.90091.00087.1021.1143.243
    7GB-YOLOv5m88.90099.00097.3013.2937.553
    下载: 导出CSV

    表  2  GB-YOLOv5m与其他算法对不同尺度目标检测精度结果对比

    序号算法APs(%)APM(%)APL(%)
    1SSD30014.56040.40096.893
    2Faster R-CNN0.78493.98099.370
    3YOLOv4-Tiny2.62466.58398.728
    4YOLOX74.56098.98797.346
    5DETR-DC530.61128.94225.440
    6YOLOv5m77.60099.53399.225
    7GB-YOLOv5m95.82299.40099.200
    下载: 导出CSV

    表  3  基于YOLOv5m模型的消融实验

    序号实验组P (%)R (%)APs (%)APM (%)APL (%)mAP (%)Param (MB)fps
    1YOLOv5m88.99177.60099.53399.22587.121.1143.243
    2YOLOv5m+ Ghost88.99076.05699.53399.05086.211.2446.180
    3YOLOv5m+BiFPN90.89179.01099.40099.40087.923.6242.517
    4YOLOv5m+4层检测层88.59994.88099.46698.90096.723.1737.355
    5YOLOv5m+ Ghost+BiFPN89.89178.73399.43399.52587.812.8243.975
    6YOLOv5m+ Ghost+4层检测层88.19994.61199.43399.15096.612.0939.124
    7YOLOv5m+ BiFPN+4层检测层90.89996.20099.40099.27597.625.4936.364
    8YOLOv5m+Ghost+BiFPN +4层检测层(GB-YOLOv5m)88.99995.82299.40099.20097.313.2937.553
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-16
  • 修回日期:  2022-07-06
  • 录用日期:  2022-07-14
  • 网络出版日期:  2022-07-21
  • 刊出日期:  2022-11-14

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