Detection Method of Insulator Breakage Based on Context Augmentation and Feature Refinement

被引：0
|
作者：
Li L. [1 ,2 ]
Zhang Y. [1 ,2 ]
Chen P. [1 ,2 ]
Ding J. [1 ,2 ]
Zhang G. [1 ,2 ]
Gong P. [3 ]
机构：
[1] Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan
[2] Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan
[3] School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan
来源：
Gaodianya Jishu/High Voltage Engineering | 2023年 / 49卷 / 08期
基金：
中国国家自然科学基金;
关键词：
breakage detection; context augmentation; deep learning; feature refinement; insulators;
D O I：
10.13336/j.1003-6520.hve.20220547
中图分类号：
学科分类号：
摘要：
Rapid and accurate detection of insulator defects is an important and challenging task for grid maintenance. Aiming at the problems of slow detection speed and high model complexity of the current mainstream insulator defect detection algorithms, we propose a breakage detection method of insulator based on context augmentation and feature refinement. In the method, firstly, the lightweight ECA-GhostNet is used as the backbone, and a lightweight adaptive context augmentation module is embedded at the output of the backbone to inject multi-scale context for the insulator breakage. Then, a fast and efficient feature refinement module are introduced at the output of the feature pyramid network(FPN) for augmenting insulator breakage defect features. Several sets of comparative experiments are carried out on the dataset constructed in this paper. The results show that the mean average precision(mAP) of the method proposed in this paper can reach about 97.05%, the detection speed is about 63 Frame/s, and the floating point operations(FLOPs) and parameters are 1.46 G and 1.68 M, respectively.All performance indicators are superior to those obtained by mainstream algorithms, such as RetinaNet, YOLOv4 and YOLOF. The research results can provide a reference for embedded applications of drone. © 2023 Science Press. All rights reserved.
引用
收藏
页码：3405 / 3414
页数：9
相关论文
共 30 条
  • [1] PARK K C, MOTAI Y, YOON J R., Acoustic fault detection technique for high-power insulators, IEEE Transactions on Industrial Electronics, 64, 12, pp. 9699-9708, (2017)
  • [2] BIAN Jianpeng, LI Fan, HAO Peixu, Et al., Damage identification and location of transmission line insulator in complex environment, High Voltage Engineering, 48, 2, pp. 681-688, (2022)
  • [3] WANG Daolei, SUN Jiajun, ZHANG Tianyu, Et al., Self-explosion defect detection method of glass insulator based on improved generative adversarial network, High Voltage Engineering, 48, 3, pp. 1096-1103, (2022)
  • [4] VARGHESE A, GUBBI J, SHARMA H, Et al., Power infrastructure monitoring and damage detection using drone captured images, Proceedings of the 2017 International Joint Conference on Neural Networks, pp. 1681-1687, (2017)
  • [5] CHENG H Y, ZHAI Y J, CHEN R, Et al., Self-shattering defect detection of glass insulators based on spatial features, Energies, 12, 3, (2019)
  • [6] LI B F, WU D L, CONG Y, Et al., A method of insulator detection from video sequence, Proceedings of the 2012 4th International Symposium on Information Science and Engineering, pp. 386-389, (2012)
  • [7] PENG Xiangyang, LIANG Fuxun, QIAN Jinju, Et al., Automatic recognition of insulator from UAV infrared image based on periodic textural feature, High Voltage Engineering, 45, 3, pp. 922-928, (2019)
  • [8] ZHAO Z B, LIU N, WANG L., Localization of multiple insulators by orientation angle detection and binary shape prior knowledge, IEEE Transactions on Dielectrics and Electrical Insulation, 22, 6, pp. 3421-3428, (2015)
  • [9] MA Yaoming, ZHANG Yu, Insulator detection algorithm based on improved Faster-RCNN, Journalof Computer Applications, 42, 2, pp. 631-637, (2022)
  • [10] TAO X, ZHANG D P, WANG Z H, Et al., Detection of power line insulator defects using aerial images analyzed with convolutional neural networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 4, pp. 1486-1498, (2020)
123