基于改进EfficientDet的电网元件及缺陷识别方法

doi:10.19595/j.cnki.1000-6753.tces.210956

Abstract
Figure/Table
References
Related Citation (3)

Download: PDF (19998 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract An improved EfficientDet target detection algorithm was proposed to deal with the problem that the existing UAV electric power inspection target detection algorithm could easily produce low accuracy of small target, the detected components and defect types are relatively single, and could not satisfy with the detection speed and accuracy simultaneously. The algorithm was applied for data mining of power inspection images to detect insulators, dampers, grading-ring, shielding-ring and bird-house on the high voltage transmission lines and locate the corresponding defects at the same time. Firstly, this paper uses Imgaug data enhancement library to enhance the data of the existing 1 468 standardized UAV inspection datasets in the State Grid. Then, to improve the small target detection ability of the bidirectional feature pyramid networks and efficiency of the backbone network EfficientNet, a feature layer with a smaller scale was integrated weighted, the backdown residual module was improved and coordinate attention mechanism was introduced. Finally, a comparative training experiment is carried out, and the improved EfficientDet has the accuracy reaching up to 90.2% on the test set, which is 8.6% higher than the original EfficientDet and other advanced target detection algorithm. Meanwhile, the frame rate per second of component inspection and defect location reaches 23.4 and 17.2 respectively. It is proved that this method can meet the requirements of accuracy and efficiency in power inspection.

Key words： Power inspection EfficientDet component detection defect location data enhancement

Received: 26 June 2021

PACS:

TM755

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Song Liye
	Liu Shuai
	Wang Kai
	Yang Jindan

Cite this article:

Song Liye,Liu Shuai,Wang Kai等. Identification Method of Power Grid Components and Defects Based on Improved EfficientDet[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2241-2251.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210956 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I9/2241

[1] 徐建军, 黄立达, 闫丽梅, 等. 基于层次多任务深度学习的绝缘子自爆缺陷检测[J]. 电工技术学报, 2021, 36(7): 1407-1415.
Xu Jianjun, Huang Lida, Yan Limei, et al.Insulator self-explosion defect detection based on hierarchical multi-task deep learning[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1407-1415.
[2] 张倩, 王建平, 李帷韬. 基于反馈机制的卷积神经网络绝缘子状态检测方法[J]. 电工技术学报, 2019, 34(16): 3311-3321.
Zhang Qian, Wang Jianping, Li Wentao.Insulator state detection of convolutional neural networks based on feedback mechanism[J]. Transactions of China Electrotechnical Society, 2019, 34(16): 3311-3321.
[3] 高伟, 周宸, 郭谋发. 基于改进YOLOv4及SR-GAN的绝缘子缺陷辨识研究[J]. 电机与控制学报, 2021, 25(11): 93-104.
Gao Wei, Zhou Chen, Guo Moufa.Insulator defect identification via improved YOLOv4 and SR-GAN algorithm[J]. Electric Machines and Control, 2021, 25(11): 93-104.
[4] 田毅, 伍逸群, 张烨, 等. 基于融合色差和神经网络的防震锤故障识别[J]. 计算机技术与发展, 2020, 30(8): 103-108.
Tian Yi, Wu Yiqun, Zhang Ye, et al.Fault identification of damper defect based on fused chromatic aberration and neural network[J]. Computer Technology and Development, 2020, 30(8): 103-108.
[5] 汤踊, 韩军, 魏文力, 等. 深度学习在输电线路中部件识别与缺陷检测的研究[J]. 电子测量技术, 2018, 41(6): 60-65.
Tang Yong, Han Jun, Wei Wenli, et al.Research on part recognition and defect detection of transmission line in deep learning[J]. Electronic Measurement Technology, 2018, 41(6): 60-65.
[6] 徐奇伟, 黄宏, 张雪锋, 等. 基于改进区域全卷积网络的高压引线接头红外图像特征分析的在线故障诊断方法[J]. 电工技术学报, 2021, 36(7): 1380-1388.
Xu Qiwei, Huang Hong, Zhang Xuefeng, et al.Online fault diagnosis method for infrared image feature analysis of high-voltage lead connectors based on improved R-FCN[J]. Transactions of China Electrotechnical Society, 2021, 36(7): 1380-1388.
[7] 卞建鹏, 郝嘉星, 赵帅, 等. 基于改进胶囊网络的接触网吊弦故障识别与定位[J]. 电工技术学报, 2020, 35(24): 5187-5196.
BianJianpeng, Hao Jiaxing, Zhao Shuai, et al. Fault identification and location of catenary suspension based on improved capsule network[J]. Transactions of China Electrotechnical Society, 2020, 35(24): 5187-5196.
[8] 邱志斌, 朱轩, 廖才波, 等. 基于目标检测的电网涉鸟故障相关鸟种智能识别[J]. 电网技术, 2022, 46(1): 369-377.
QiuZhibin, Zhu Xuan, Liao Caibo, et al. Intelligent recognition of bird species related to power grid faults based on object detection[J]. Power System Technology, 2022, 46(1): 369-377.
[9] 江波, 屈若锟, 李彦冬, 等. 基于深度学习的无人机航拍目标检测研究综述[J]. 航空学报, 2021, 42(4): 137-151.
Jiang Bo, Qu ruokun, Li Yandong, et al. Object detection in UAV imagery based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 137-151.
[10] 张培培, 王昭, 王菲. 基于深度学习的图像目标检测算法研究[J]. 国外电子测量技术, 2020, 39(8): 34-39.
Zhang Peipei, Wang Zhao, Wang Fei.Research on image target detection algorithm based on deep learning[J]. Foreign Electronic Measurement Technology, 2020, 39(8): 34-39.
[11] Liu Wei, Anguelov D, Erhan D, et al.SSD: Single shot multibox detector[C]//European Conference on Computer Vision, Springer, Cham, 2016: 21-37.
[12] Bochkovskiy A, Wang C Y, Liao H.YOLOv4: optimal speed and accuracy of object detection[J]. arXiv Preprint arXiv: 2004. 10934, 2020.
[13] Ren Shaoqing, He Kaiming, 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.
[14] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[15] Feldman A M, Dai Z, Carver E, et al.Utilizing a deep learning-based object detection and instance segmentation algorithm for the delineation of prostate and prostate cancer segmentation[J]. International Journal of Radiation Oncology*Biology*Physic, 2019, 105(1): S197-S198.
[16] 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.
[17] 蒋欣兰, 贾文博. 高铁接触网异物侵入的机器视觉检测方法[J]. 计算机工程与应用, 2019, 55(22): 250-257.
Jiang Xinlan, Jia Wenbo.Machine vision detection method for foreign object intrusion in high-speed rail contact net[J]. Computer Engineering and Applications, 2019, 55(22): 250-257.
[18] 缪希仁, 林志成, 江灏, 等. 基于深度卷积神经网络的输电线路防鸟刺部件识别与故障检测[J]. 电网技术, 2021, 45(1): 126-133.
Miao Xiren, Lin Zhicheng, Jiang Hao, et al.Fault detection of power tower anti-bird spurs based on deep convolutional neural network[J]. Power System Technology, 2021, 45(1): 126-133.
[19] 刘逸凡, 王淑青, 庆毅辉, 等. 基于EfficientDet和双目摄像头的绝缘子缺陷检测[J]. 中国电力, 2021, 54(2): 156-163, 196.
Liu Yifan, Wang Shuqing, Qing Yihui, et al.Insulator defect detection based on EfficientDet and binocular camera[J]. Electric Power, 2021, 54(2): 156-163, 196.
[20] Jiang Hao, Huang Wulin, Chen Jing, et al.Detection of bird nests on power line patrol using single shot detectorr[C]//2019 Chinese Automation Congress (CAC), Hangzhou, China, 2019: 3409-3414.
[21] Wang Jiwu, Zhang Xinying, Zheng Lele, et al.A study on the grading ring recognition method of power line based on deep learning[C]//2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT), Busan, Korea(South), 2018: 1-4.
[22] 易继禹, 陈慈发, 龚国强. 基于改进Faster RCNN的输电线路航拍绝缘子检测[J]. 计算机工程, 2021, 47(6): 292-298, 304.
Yi Jiyu, Chen Cifa, Gong Guoqiang.Aerial insulator detection of transmission line based on improved Faster RCNN[J]. Computer Engineering, 2021, 47(6): 292-298, 304.
[23] Huang Xinbo, Zhang Xiaoling, Zhang Ye, et al.A method of identifying rust status of dampers based on image processing[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(8): 5407-5417.
[24] 林成创, 单纯, 赵淦森, 等. 机器视觉应用中的图像数据增广综述[J]. 计算机科学与探索, 2021, 15(4): 583-611.
Lin chengchuang, Chun Chun, Zhao gansen, et al. Review of image data augmentation in computer vision[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(4): 583-611.
[25] 王珂珂. 基于样本生成的输电线路多目标检测算法研究[D]. 葫芦岛: 辽宁工程技术大学, 2020.
[26] 武凯利. 复杂气象条件下的航拍目标检测[D]. 北京: 中国人民公安大学, 2019.
[27] Tan Mingxing, Pang Ruoming, Le Q V.EfficientDet: Scalable and efficient object detection[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020: 10781-10790.
[28] Tan Mingxing, Le QuocV. EfficientNet: rethinking model scaling for convolutional neural networks[J]. International Conference on MachineLearning, 2019: arXiv: 1905. 11946v5.
[29] Zhou Daquan, Hou Qibin, Chen Yunpeng, et al.Rethinking bottleneck structure for efficient mobile network design[J]. arXiv Preprint arXiv: 2007. 02269, 2020.
[30] Hou Qibin, Zhou Daquan, Feng Jiashi.Coordinate attention for efficient mobile network design[J]. arXiv Preprint arXiv: 2103. 02907, 2021.