Fault detection for overhead line power equipment based on improved YOLOv4
Author:
Affiliation:

(1.School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China;2.Guilin Power Supply Bureau, Guangxi Power Gird Co., Ltd., Guilin 541000, China)

Clc Number:

TM50,TP 391.4

  • Article
  • | |
  • Metrics
  • |
  • Reference [17]
  • | | | |
  • Comments
    Abstract:

    Aiming at the problems of low detection accuracy and slow speed of traditional target detection algorithms, an improved YOLOv4 target detection model is proposed to detect four types of common power equipment and faults in overhead lines, such as poles, transformers, pole tilts, and insulators drop. Instead of the backbone network in the original YOLOv4, MobileNet which is designed for the embedded platform is deployed in this model, making this model lightweight. In order to further reduce the computational complexity and strengthen the learning ability of the convolutional neural network, a deep separable convolution and a CSP structure is introduced in the neck network. This improved model is used to conduct experiments on the overhead line image data set, and the experimental results show that this model can increase the detection speed to 1.68 times of the original model with a equivalent detection accuracy. It can be better applied to embedded devices, and thus achieves the real?time detection of common power equipment and faults in overhead lines by drones.

    Reference
    [1] 殷浩然,苗世洪,韩佶,等.基于三维卷积神经网络的配电物联网异常辨识方法[J].电力系统自动化,2022,46(1):42?50. YIN Haoran,MIAO Shihong,HAN Ji,et al.Anomaly identification method for distribution Internet of Things based on three?dimensional convolutional neural network[J].Automation of Electric Power Systems,2022,46(1):42?50.
    [2] 郑含博,李金恒,刘洋,等.基于改进YOLOv3的电力设备红外目标检测模型[J].电工技术学报,2021,36(7):1389?1398. ZHENG Hanbo,LI Jinheng,LIU Yang,et al.Infrared object detection model for power equipment based on improved YOLOv3[J].Transactions of China Electrotechnical Society,2021,36(7):1389?1398.
    [3] 黄雪莜,熊俊,张宇,等.基于残差卷积神经网络的开关柜局部放电模式识别[J].中国电力,2021,54(2):44?51. HUANG Xueyou,XIONG Jun,ZHANG Yu,et al.Partial discharge pattern recognition of switchgear based on residual convolutional neural network[J].Electric Power,2021,54(2):44?51.
    [4] 唐睿,张铭予,徐宏,等.基于深度学习的输电线路巡检图像绝缘子识别[J].电网与清洁能源,2021,37(4):41?46. TANG Rui,ZHANG Mingyu,XU Hong,et al.Insulator recognition in transmission line inspection image based on deep learning[J].Power System and Clean Energy,2021,37(4):41?46.
    [5] 黄新宇,张洋,王黎明,等.基于Mask?RCNN算法的复合绝缘子串红外图像分割与温度读取[J].高压电器,2021,57(9):87?94. HUANG Xinyu,ZHANG Yang,WANG Liming,et al.Infrared image segmentation and temperature reading of composite insulator strings based on Mask?RCNN algorithm[J].High Voltage Apparatus,2021,57(9):87?94.
    [6] 何宁辉,王世杰,刘军福,等.基于深度学习的航拍图像绝缘子缺失检测方法研究[J].电力系统保护与控制,2021,49(12):132?140. HE Ninghui,WANG Shijie,LIU Junfu,et al.Research on infrared image missing insulator detection method based on deep learning[J].Power System Protection and Control,2021,49(12):132?140.
    [7] GIRSHICK R.Fast R?CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV),Santiago,Chile,2016:1440?1448.
    [8] REN S Q,HE K M,GIRSHICK R,et al.Faster R?CNN:towards real?time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137?1149.
    [9] 黄玲,赵锴,李继东,等.基于特征金字塔和多任务学习的绝缘子图像检测[J].电测与仪表,2021,58(4):37?43. HUANG Ling,ZHAO Kai,LI Jidong,et al.Insulator image detection based on feature pyramid and multi?task learning[J].Electrical Measurement & Instrumentation,2021,58(4):37?43.
    [10] 韩谷静,何敏,雷宇航,等.基于改进U?Net的输电线路绝缘子图像分割方法研究[J].智慧电力,2022,50(3):93?99. HAN Gujing,HE Min,LEI Yuhang,et al.Image segmentation method of transmission line insulator based on improved U?net[J].Smart Power,2022,50(3):93?99.
    [11] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real?time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Las Vegas,NV,USA,2016:779?788.
    [12] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot MultiBox detector[C]//European Conference on Computer Vision,Amsterdam,The Hetherlands,2016:21?37.
    [13] ZHOU X Y,WANG D Q,KR?HENBüHL P.Objects as points[EB/OL].[2021?10?19].http://arxiv.org/abs/1904.07850. pdf
    [14] 杨焰飞,曹阳.改进YOLOv3的无人机拍摄图玻璃绝缘子检测[J].计算机工程与应用,2022,58(3):259?265. YANG Yanfei,CAO Yang.Detection of glass insulators in images taken by drones based on improved YOLOv3[J].Computer Engineering and Applications,2022,58(3):259?265.
    [15] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),Honolulu,HI,USA,2017:6517?6525.
    [16] BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:optimal speed and accuracy of object detection[EB/OL].2020:arXiv:2004.10934.http://arxiv.org/abs/2004. 10934.pdf
    [19] HOWARD A,SANDLER M,CHEN B,et al.Searching for MobileNetV3[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV),Seoul,Korea,2020:1314?1324.
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

彭曙蓉,刘登港,何洁妮,陆 双,苏 盛,贺 鸣.基于改进YOLOv4的架空线路电力设备故障检测[J].电力科学与技术学报英文版,2023,38(5):169-176. PENG Shurong, LIU Denggang, HE Jieni, LU Shuang, SU Sheng, HE Ming. Fault detection for overhead line power equipment based on improved YOLOv4[J]. Journal of Electric Power Science and Technology,2023,38(5):169-176.

Copy
Share
Article Metrics
  • Abstract:215
  • PDF: 0
  • HTML: 0
  • Cited by: 0
History
  • Online: January 15,2024
Article QR Code