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深度学习在绝缘子红外图像异常诊断的应用

范鹏 冯万兴 周自强 赵淳 周盛 姚翔宇

范鹏, 冯万兴, 周自强, 赵淳, 周盛, 姚翔宇. 深度学习在绝缘子红外图像异常诊断的应用[J]. 红外技术, 2021, 43(1): 51-55.
引用本文: 范鹏, 冯万兴, 周自强, 赵淳, 周盛, 姚翔宇. 深度学习在绝缘子红外图像异常诊断的应用[J]. 红外技术, 2021, 43(1): 51-55.
FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. INFRARED TECHNOLOGY, 2021, 43(1): 51-55.
Citation: FAN Peng, FENG Wanxing, ZHOU Ziqiang, ZHAO Chun, ZHOU Sheng, YAO Xiangyu. Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis[J]. INFRARED TECHNOLOGY, 2021, 43(1): 51-55.

深度学习在绝缘子红外图像异常诊断的应用

基金项目: 

国网电力科学研究院有限公司科技项目 524625190054

详细信息
    作者简介:

    范鹏(1986-),男,硕士,高级工程师,主要从事电网智能运检、电力物联网与人工智能方面的技术研究工作。E-mail: fanpeng2@sgepri.sgcc.com.cn

  • 中图分类号: TN219

Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

  • 摘要: 绝缘子的红外图像分析一般采用图像处理的方法,易受背景环境和数据量的影响,准确率和效率均较低,本文提出一种深度学习的异常诊断方法,基于改进的Faster R-CNN方法搭建检测网络,开展不同类型的绝缘子测试。研究结果表明:相对于神经网络(Back Propagation,BP)、Faster R-CNN方法,本文方法可高效地诊断出绝缘子的异常缺陷,平均检测精度达到90.2%;单Ⅰ型和Ⅴ型绝缘子的异常诊断准确率高于双Ⅰ型绝缘子。研究结果可为输电线路绝缘子异常诊断提供一定的参考。
  • 图  1  Faster R-CNN的算法流程

    Figure  1.  Algorithm flow of Faster R-CNN

    图  2  正负样本判定

    Figure  2.  Positive and negative sample decision

    图  3  改进的Faster R-CNN结构

    Figure  3.  Structure of improved Faster R-CNN

    图  4  准确率-召回率关系曲线

    Figure  4.  Relation curves of precision and recall

    图  5  不同类型绝缘子的红外图像

    Figure  5.  Infrared image of different types of insulators

    表  1  软硬件配置

    Table  1.   Hardware and software configuration

    Name Model
    Operating system Ubuntu 16.04.1
    Database mysql 5.5.20
    CPU Intel Xeon Silver 4114T 12C
    GPU NVIDIA GTX1080Ti
    Memory 32 G
    Hard disk 1 T
    Frame Detectron
    下载: 导出CSV

    表  2  样本配置信息

    Table  2.   Information of sample configuration

    sample type training set verification set test set total
    positive 500 250 750 1500
    negative 500 250 125 875
    total 1000 500 875 2375
    下载: 导出CSV

    表  3  不同方法的实验结果统计

    Table  3.   Statistics of experimental results by different methods

    Name Precision Recall mAP Time/s
    BP 93.5% 90.4% 80.3% 2.3
    Faster R-CNN 98.7% 95.3% 88.7% 1.2
    BFEM 99.2% 97.6% 90.2% 0.9
    下载: 导出CSV

    表  4  绝缘子异常诊断的准确率

    Table  4.   Accuracy of insulator anomaly diagnosis

    Insulator type Abnormal total Detected number Accuracy
    Single Ⅰ 62 61 98.4%
    Double Ⅰ 47 44 93.6%
    31 31 100.0%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-08
  • 修回日期:  2020-11-22
  • 刊出日期:  2021-01-20

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