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基于深度残差UNet网络的电气设备红外图像分割方法

刘赫 赵天成 刘俊博 矫立新 许志浩 袁小翠

刘赫, 赵天成, 刘俊博, 矫立新, 许志浩, 袁小翠. 基于深度残差UNet网络的电气设备红外图像分割方法[J]. 红外技术, 2022, 44(12): 1351-1357.
引用本文: 刘赫, 赵天成, 刘俊博, 矫立新, 许志浩, 袁小翠. 基于深度残差UNet网络的电气设备红外图像分割方法[J]. 红外技术, 2022, 44(12): 1351-1357.
LIU He, ZHAO Tiancheng, LIU Junbo, JIAO Lixin, XU Zhihao, YUAN Xiaocui. Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment[J]. Infrared Technology , 2022, 44(12): 1351-1357.
Citation: LIU He, ZHAO Tiancheng, LIU Junbo, JIAO Lixin, XU Zhihao, YUAN Xiaocui. Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment[J]. Infrared Technology , 2022, 44(12): 1351-1357.

基于深度残差UNet网络的电气设备红外图像分割方法

基金项目: 

国网吉林省电力有限公司揭榜挂帅项目 2021JBGS-06

详细信息
    作者简介:

    刘赫(1984-),男,吉林长春人,高级工程师,研究方向为电力设备故障检测与诊断。E-mail: liuhehe1984@163.com

    通讯作者:

    赵天成(1992-),男,吉林长春人,工程师,硕士,研究方向为电力设备故障检测与诊断。E-mail: 583107503@qq.com

  • 中图分类号: TN219;TM452

Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment

  • 摘要: 红外图像处理是实现电气故障诊断的有效手段,而电气设备分割是故障检测的关键环节。针对复杂背景下红外图像电气设备分割难问题,本文采用深度残差网络与UNet网络相结合,深度残差网络替代VGG16对UNet网络进行特征提取和编码,构建深度残差系列Res-Unet网络实现对电气设备的分割。以电流互感器和断路器两种电气设备红外图像分割为例测试Res-Unet网络分割效果,并与传统的UNet网络和Deeplabv3+网络进行对比。通过对数量为876的样本进行测试,实验结果表明,Res18-UNet能够准确地分割电气设备,对电流互感器和断路器的分割准确率超93%,平均交并比大于89%,且分割准确性优于UNet及Deeplabv3+网络模型,为实现电气故障智能诊断奠定基础。
  • 图  1  样本增强示例

    Figure  1.  Example of sample images enhancement

    图  2  样本图像标签

    Figure  2.  Labels of image samples

    图  3  UNet网络结构

    Figure  3.  UNet network structure

    图  4  ResNet网络结构

    Figure  4.  ResNet network structure

    图  5  改进UNet网络结构

    Figure  5.  Improved UNet network structure

    图  6  网络训练过程损失函数对比

    Figure  6.  Comparison of loss functions for network training

    图  7  简单背景下电流互感器分割结果

    Figure  7.  Segmentation results of current transformer with simple background

    图  8  复杂背景下电流互感器分割结果

    Figure  8.  Segmentation results of current transformer with complex background

    图  9  背景干扰下断路器分割结果

    Figure  9.  Segmentation results of circuit breaker image with complex background

    图  10  局部遮挡下断路器分割结果

    Figure  10.  Segmentation results of circuit breaker with local occlusion

    表  1  不同分割方法得到的MIOU值

    Table  1.   The MIOU values based on different segmentation methods

    Image and network Deeplabv3+ UNet Res18-UNet Res34-UNet Res50-UNet
    Fig.8 0.7893 0.8209 0.9315 0.7798 0.6218
    Fig.9 0.7768 0.7871 0.8839 0.8184 0.6637
    Fig.10 0.7919 0.8309 0.8936 0.7301 0.6328
    Fig.11 0.7888 0.8268 0.9057 0.7165 0.6581
    下载: 导出CSV

    表  2  测试数据集的准确率

    Table  2.   The accuracy of the test dataset

    network Segmentation object IoU MIoU Precision
    Deeplabv3+ Current transformer 0.79 0.8011 0.90
    Circuit breaker 0.67 0.84
    Background 0.95 0.97
    UNet Current transformer 0.8023 0.8272 0.9150
    Circuit breaker 0.7179 0.8960
    Background 0.9615 0.9805
    Res18-UNet Current transformer 0.8623 0.8963 0.9470
    Circuit breaker 0.8579 0.9347
    Background 0.9686 0.9907
    Res34-UNet Current transformer 0.6306 0.7139 0.7110
    Circuit breaker 0.6064 0.7396
    Background 0.9047 0.9872
    Res50-UNet Current transformer 0.4747 0.5906 0.5174
    Circuit breaker 0.4249 0.3700
    Background 0.8722 0.9689
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-25
  • 修回日期:  2022-04-29
  • 刊出日期:  2022-12-20

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