留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进高斯卷积核的变电站设备红外图像检测方法

吴添权 郭竞 苟先太 黄勤琴 周维超

吴添权, 郭竞, 苟先太, 黄勤琴, 周维超. 基于改进高斯卷积核的变电站设备红外图像检测方法[J]. 红外技术, 2021, 43(3): 230-236.
引用本文: 吴添权, 郭竞, 苟先太, 黄勤琴, 周维超. 基于改进高斯卷积核的变电站设备红外图像检测方法[J]. 红外技术, 2021, 43(3): 230-236.
WU Tianquan, GUO Jing, GOU Xiantai, HUANG Qinqin, ZHOU Weichao. Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel[J]. INFRARED TECHNOLOGY, 2021, 43(3): 230-236.
Citation: WU Tianquan, GUO Jing, GOU Xiantai, HUANG Qinqin, ZHOU Weichao. Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel[J]. INFRARED TECHNOLOGY, 2021, 43(3): 230-236.

基于改进高斯卷积核的变电站设备红外图像检测方法

基金项目: 

四川省人工智能重大专项项目 2018GZDZX0043

中国南方电网科技项目 035100KK52190003

详细信息
    作者简介:

    吴添权(1977-),男,广东潮州人,高级工程师,主要从事电气设备试验工作,主要研究方向为电气试验技术

    通讯作者:

    苟先太(1971-),男,四川三台人,副教授,博士,主要研究方向为电网智能化、人工智能技术。E-mail: 491098063@qq.com

  • 中图分类号: TM727

Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel

  • 摘要: 在无锚点算法CenterNet模型的基础上,针对基于红外图像的目标检测算法检测精度低、耗时长的问题,给出了一种基于改进高斯卷积核的变电站设备红外图像检测方法,该目标检测方法模型网络结构精简,模型计算量较小。通过现场变电站巡检机器人设备收集数据样本,进行算法模型的训练及验证,实现红外图像变电站设备精准识别及定位。本文以变电站巡检机器人搭配红外热成像仪采集到的红外图像库为基础,用深度学习方法对数据集进行训练和测试,研究变电站红外图像的目标检测技术。通过深度学习技术判断设备中心点位实现目标分类和回归。实验结果表明,该方法提高了变电站目标检测方法的识别定位精度,为变电站设备红外图像智能检测提供了新的思路。
  • 图  1  训练数据集部分样本

    Figure  1.  Somesamples of the training data set

    图  2  目标检测流程图

    Figure  2.  Flow chart of target detection

    图  3  通过中心点预测候选框

    Figure  3.  The candidate box is predicted by the center point

    图  4  多个满足条件的候选框

    Figure  4.  Several candidate boxes that satisfy the criteria

    图  5  预测框与GT box三种不同位置关系图

    Figure  5.  Three different position relation diagrams of prediction box and GT box

    图  6  高斯卷积核改进前后的Boundingbox和Heatmap示意图

    Figure  6.  Bounding box and Heat map before and after the improvement of Gaussian convolution kernel

    图  7  σ=1的高斯核函数

    Figure  7.  The Gaussian kernel at σ=1

    图  8  σ=5的高斯核函数

    Figure  8.  The Gaussian kernel at σ=5

    图  9  部分检测结果图

    Figure  9.  Some detection result graph

    表  1  红外图像数据集

    Table  1.   Infrared image data set

    Classname Label name Picture numbers
    Arrester Arrester 123
    Breaker Breaker 166
    Current transformer Current transformer 137
    Disconnector Disconnector 73
    Electricreactor Electricreactor 150
    Voltage transformer Voltage transformer 303
    Aerial conductor Aerial conductor 86
    Condenser Condenser 236
    Main transformer Main transformer 224
    Tubular busbar Tubular busbar 72
    下载: 导出CSV

    表  2  计算机硬件配置

    Table  2.   Computer hardware configuration

    Name Type
    CPU Intel Core I7 9700K
    GPU Nvidia RTX 2080 Ti
    hard disk 4T SAS 7.2K*1
    memory 512 G
    下载: 导出CSV

    表  3  红外图像数据集测试结果

    Table  3.   Test results of infrared image data set

    bn Model mAP Epoch hm_loss wh_loss off_loss Loss
    DLA-34 before 0.685 200 1.2178887 3.385998 0.229195 1.646682
    after 0.705 200 1.1233817 2.775918 0.212195 1.415212
    Res-101 before 0.661 200 0.396270 3.179799 0.244151 1.138400
    after 0.723 200 0.521400 1.897403 0.232067 1.043208
    Res-18 before 0.463 200 0.451421 2.713203 0.262212 0.994953
    after 0.582 200 0.813421 2.113203 0.256721 0.87198
    下载: 导出CSV

    表  4  针对变电站真实条件下的性能试验记录表

    Table  4.   Performance test record for substation

    Type Picture numbers Target numbers Correct detection
    number
    Average accuracy Miss raio Fallout ratio Total time
    Aerialconductor 12 16 12 0.750 0.250 0 1.080
    Arrester 38 62 54 0.871 0.129 0 3.040
    Breaker 45 125 114 0.912 0.024 0.064 4.562
    Condenser 83 83 71 0.855 0.133 0.012 8.088
    Currenttransformer 33 91 83 0.912 0.030 0.058 2.699
    Disconnector 16 16 13 0.813 0.187 0 1.746
    Electricreactor 34 65 56 0.862 0.138 0 2.919
    Maintransformer 42 42 36 0.857 0.143 0 3.606
    Tubular busbar 8 15 12 0.800 0.200 0 0.874
    Voltagetransformer 76 146 133 0.911 0.048 0.041 6.519
    下载: 导出CSV
  • [1] Junwei Hsieh, Yungtai Hsu, Hongyuan Mark Liao, et al. Video-based human movement analysis and its application to surveillance systems[J]. IEEE Transactions on Multimedia, 2008, 10(3): 372-384. doi:  10.1109/TMM.2008.917403
    [2] 黄文清, 汪亚明, 周志宇. 计算机视觉技术在工业领域中的应用[J]. 浙江理工大学学报: 自然科学版, 2002, 19(2): 28-32. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJSG200202005.htm

    HUANG Wenqing, WANG Yaming, ZHOU Zhiyu. Application of computer vision technology in the field of industry[J]. Journal of Zhejiang Institute of Science and Technology, 2002, 19(2): 28-32. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJSG200202005.htm
    [3] Leonid Sigal, Michael J Black. Guest editorial: state of the art in image- and video-based human pose and motion estimation[J]. International Journal of Computer Vision, 2010, 87(1): 1-3. http://dl.acm.org/citation.cfm?id=1713483
    [4] Yuki Kitahara, Seiji Takahashi, Noriyuki Kuramoto. Ion attachment mass spectrometry combined with infrared image furnace for thermal analysis: evolved gas analysis studies[J]. Analytical Chemistry, 2009, 81(8): 3155-3158. doi:  10.1021/ac802746d
    [5] 王勇, 梅生伟, 何光宇. 变电站一次设备数字化特征和实现[J]. 电力系统自动化, 2010, 34(13): 94-99. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201013019.htm

    WANG Yong, MEI Shengwei, HE Guangyu. The characteristics and realization of digitization of primary equipment in substations[J]. Automation of Electric Power Systems, 2010, 34(13): 94-99. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT201013019.htm
    [6] Felzenszwalb P F, Girshick R B, Mcallester D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Software Engineering, 2010, 32(9): 1627-1645. http://ieeexplore.ieee.org/document/5255236
    [7] 方路平, 何杭江, 周国民. 目标检测算法研究综述[J]. 计算机工程与应用, 2018, 54(13): 11-18. doi:  10.3778/j.issn.1002-8331.1804-0167

    FANG Luping, HE Hangjiang, ZHOU Guomin. Research overview of object detection methods[J]. Computer Engineering and Applications, 2018, 54(13): 11-18. doi:  10.3778/j.issn.1002-8331.1804-0167
    [8] 阮激扬. 基于YOLO的目标检测算法设计与实现[D]. 北京: 北京邮电大学, 2019.

    RUAN Jiyang. Design and Implementation of Target Detection Algorithm Based on YOLO[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
    [9] 李鹏飞, 刘瑶, 李珣, 等. YOLO9000模型的车辆多目标视频检测系统研究[J]. 计算机测量与控制, 2019, 27(8): 21-24. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201908006.htm

    LI Pengfei, LIU Yao, LI Xun, et al. A detection method of multi-target for vehicles based on YOLO9000 model[J]. Computer Measurement & Control, 2019, 27(8): 21-24. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201908006.htm
    [10] 季航, 贾镕, 刘晓, 等. 一种基于YOLOv3的红外目标检测系统[J]. 电子设计工程, 2019, 27(22): 61-64. https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201922013.htm

    JI Hang, JIA Rong, LIU Xiao, et al. An infrared target detection system based on YOLOv3[J]. Electronic Design Engineering, 2019, 27(22): 61-64. https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201922013.htm
    [11] 栾浩, 王力, 姜敏, 等. 基于改进SSD的目标检测方法[J]. 软件, 2020, 41(1): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-RJZZ202001008.htm

    LUAN Hao, WANG Li, JIANG Min, et al. Object detection method based on improved SSD[J]. Computer Engineering & Software, 2020, 41(1): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-RJZZ202001008.htm
    [12] 谢奇芳, 姚国清, 张猛. 基于Faster R-CNN的高分辨率图像目标检测技术[J]. 国土资源遥感, 2019, 31(2): 38-43. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG201902006.htm

    XIE Qifang, YAO Guoqing, ZHANG Meng. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2019, 31(2): 38-43. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG201902006.htm
    [13] DUAN K, BAI S, XIE L, et al. CenterNet: keypoint triplets for object detection[C]//IEEE International Conference on Computer Vision, 2019: 6569-6578.
    [14] 杨海燕, 蒋新华, 聂作先. 基于并行卷积神经网络的人脸关键点定位方法研究[J]. 计算机应用研究, 2015, 32(8): 283-285. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201508068.htm

    YANG Haiyan, JIANG Xinhua, NIE Zuoxian. Facial key points location based on parallel convolutional neural network[J]. Application Research of Computers, 2015, 32(8): 283-285. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201508068.htm
    [15] 刘云鹏, 裴少通, 武建华, 等. 基于深度学习的输变电设备异常发热点红外图片目标检测方法[J]. 南方电网技术, 2019, 13(2): 27-33. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW201902006.htm

    LIU Yunpeng, PEI Shaotong, WU Jianhua, et al. Deep learning based target detection method for abnormal hot spots infrared images of transmission and transformation equipment[J]. Southern Power System Technology, 2019, 13(2): 27-33. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW201902006.htm
    [16] 林海波, 王浩, 张毅. 改进高斯核函数的人体姿态分析与识别[J]. 智能系统学报, 2015, 10(3): 436-441. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNXT201503021.htm

    LIN Haibo, WANG Hao, ZHANG Yi. Human postures recognition based on the improved Gauss kernel function[J]. CAAL Transactions on Intelligent Systems, 2015, 10(3): 436-441. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNXT201503021.htm
    [17] WU Jiajun, XUE Tianfan, Joseph J. Lim. Single image 3D interpreter network[C]// European Conference on Computer Vision, 2016: 1-18.
    [18] 莫邵文, 邓新蒲, 王帅, 等. 基于改进视觉背景提取的运动目标检测算法[J]. 光学学报, 2016(6): 196-205. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201606025.htm

    MO Shaowen, DENG Xinpu, WANG Shuai, et al. Moving object detection algorithm based on improved visual background extractor[J]. Acta Optica Sinica, 2016(6): 196-205. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201606025.htm
    [19] 朱伯伟, 庾农, 肖松. 红外极小目标检测算法研究[J]. 航空电子技术, 2011, 42(3): 5-11. https://www.cnki.com.cn/Article/CJFDTOTAL-HKDZ201103001.htm

    ZHU Bowei, YU Nong, XIAO Song. Approaches on infrared point targets detection algorithm[J]. Avionics Technology, 2011, 42(3): 5-11. https://www.cnki.com.cn/Article/CJFDTOTAL-HKDZ201103001.htm
  • 加载中
图(9) / 表(4)
计量
  • 文章访问数:  182
  • HTML全文浏览量:  71
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-04-16
  • 修回日期:  2020-12-28
  • 刊出日期:  2021-04-02

目录

    /

    返回文章
    返回