基于边缘计算与深度学习的输电设备异物检测方法

doi:10.11930/j.issn.1004-9649.201910011

中国电力 ›› 2020, Vol. 53 ›› Issue (6): 27-33.DOI: 10.11930/j.issn.1004-9649.201910011

• 人工智能在电力系统的应用 • 上一篇下一篇

基于边缘计算与深度学习的输电设备异物检测方法

路艳巧¹, 孙翠英¹, 曹红卫², 闫红伟²

1. 国网河北省电力有限公司电力科学研究院，河北石家庄 050021;
2. 国网河北省电力有限公司石家庄供电分公司，河北石家庄 050000

收稿日期:2019-10-10 修回日期:2020-02-10 发布日期:2020-06-05
作者简介:路艳巧(1987-),女,通信作者,硕士,高级工程师,从事输电线路复合外绝缘及接地研究,E-mail:1131980686@qq.com;孙翠英(1987-),女,硕士,高级工程师,从事输电线路复合外绝缘及接地研究,E-mail:363663690@qq.com;曹红卫(1983-),女,硕士,工程师,从事变电站运行、维护及其相关研究,E-mail:542279566@qq.com;闫红伟(1989-),男,硕士,工程师,从事变电检修与检测研究,E-mail:yanhongwei51@163.com
基金资助:
国网河北省电力有限公司电力科学研究院项目(基于深度学习技术的电网设备图像识别与故障检测技术研究，KJKF-20)

Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning

LU Yanqiao¹, SUN Cuiying¹, CAO Hongwei², YAN Hongwei²

1. State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China;
2. Shijiazhuang Power Supply Branch of State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China

Received:2019-10-10 Revised:2020-02-10 Published:2020-06-05
Supported by:
This work is supported by State Grid Hebei Electric Power Research Institute Project (Research on Power Grid Equipment Image Recognition and Fault Detection Technology Based on Deep Learning Technology, No.KJKF-20)

摘要/Abstract

摘要： 在输电设备上经常会出现各种异物，如鸟巢、塑料袋，如果不能及时发现并清理将会对输电系统造成很大的安全隐患。因此，及时对输电设备是否有异物进行检测非常必要。针对该问题，提出了一种基于边缘计算和深度学习的异物检测方法。该方法与现有利用无人机拍摄传回云端服务器计算方法不同，通过将检测计算下沉到边缘设备，使用Mobilenet加上优化后SSD的目标检测方法在边缘设备直接处理计算，将检测出异物的图像发回云端。该方法在CPU上的运行速度是基于VGG（目视图像生成器）的SSD方法的5倍左右，是Faster-RCNN的58倍左右；在模型大小上是基于VGG的SSD方法的2/9左右，是Faster-RCNN的2/49左右，精确度为89%；与直接将数据传回云端服务器再进行处理的方式相比，数据传输量减少约90%。该方法不仅满足实时性，还具有可靠的效果，基于该方法的系统已经得到实际部署。

关键词: 异物检测, 边缘计算, 卷积神经网络, Mobilenet, SSD

Abstract: Various foreign bodies, such as bird's nests and plastic bags, often appear on transmission equipment. Failure to detect and clean them up in time will cause great potential safety hazards to the transmission system. Therefore, it is necessary to timely detect the presence of foreign bodies on transmission equipment. To solve this problem, a foreign body detection method is proposed based on edge computing and deep learning. Different from the existing method that sends UAV pictures back to the cloud server for processing, this method, by sinking the detection calculation to the edge device, uses the target detection method of Mobilenet and optimized SSD to directly make process calculation in the edge device, and sends the pictures of detected foreign bodies back to the cloud server. The proposed method is about 5 times faster than the VGG-based SSD method and 58 times faster than the Faster-RCNN method in CPU running speed, and 2/9 times of the VGG-based SSD method and 2/29 times of the Faster-RCNN method in model size, with an accuracy of 89%. Compared with the method that sends original data back to the cloud server for processing, the proposed method can reduce the data transmission amount by about 90%. It is concluded that the proposed method can reliably detect foreign bodies on transmission equipment in real time. The detecting system based on this method has been deployed in practice.

Key words: foreign body detection, edge computing, convolutional neural network, Mobilenet, single shot multibox detector(SSD)

路艳巧, 孙翠英, 曹红卫, 闫红伟. 基于边缘计算与深度学习的输电设备异物检测方法[J]. 中国电力, 2020, 53(6): 27-33.

LU Yanqiao, SUN Cuiying, CAO Hongwei, YAN Hongwei. Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning[J]. Electric Power, 2020, 53(6): 27-33.

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https://www.electricpower.com.cn/CN/Y2020/V53/I6/27

参考文献

[1] 龚钢军, 张帅, 吴秋新, 等. 基于TensorFlow的高压输电线路异物识别[J]. 电力自动化设备, 2019, 39(4): 204-209, 216
GONG Gangjun, ZHANG Shuai, WU Qiuxin, et al. Foreign body identification based on TensorFlow for high voltage transmission line[J]. Electric Power Automation Equipment, 2019, 39(4): 204-209, 216
[2] 赵雨田. 基于数字图像处理的输电线路异物识别技术研究[D]. 南京: 南京理工大学, 2017.
ZHAO Yutian. Research on power line foreign body recognition based on digital image processing[D]. Nanjing: Nanjing University of Science and Technology, 2017.
[3] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition (CVPR'05). San Diego: IEEE, 2005: 886-893.
[4] HE K M, ZHANG X Y, REN S Q, 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.
[5] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[EB/OL]. (2015-06-08) [2019-08-08]. https://arxiv.org/abs/1506.02640.
[6] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proc of European Conference on Computer Vision. Berlin: Springer, 2016: 21-37.
[7] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014. Columbus, OH, USA. IEEE, 2014.
[8] GIRSHICK R.Fast R-CNN[C]//Proc of IEEE International Conference on Computer Vision.Sydney: Institute of Electrical and Electronics Engineers Inc, 2015: 1440-1448.
[9] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]//Advances in Neural Information Processing Systems. Montreal: MIT Press, 2015: 91–99.
[10] QI S X, MA J, LIN J, et al. Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1451-1455.
[11] CORBANE C, NAJMAN L, PECOUL E, et al. A complete processing chain for ship detection using optical satellite imagery[J]. International Journal of Remote Sensing, 2010, 31(22): 5837-5854.
[12] 施巍松, 孙辉, 曹杰, 等. 边缘计算: 万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54(5): 907-924
SHI Weisong, SUN Hui, CAO Jie, et al. Edge computing: an emerging computing model for the Internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907-924
[13] KUMAR N, ZEADALLY S, RODRIGUES J J P C. Vehicular delay-tolerant networks for smart grid data management using mobile edge computing[J]. IEEE Communications Magazine, 2016, 54(10): 60-66.
[14] 李彬, 贾滨诚, 曹望璋, 等. 边缘计算在电力需求响应业务中的应用展望[J]. 电网技术, 2018, 42(1): 79-87
LI Bin, JIA Bincheng, CAO Wangzhang, et al. Application prospect of edge computing in power demand response business[J]. Power System Technology, 2018, 42(1): 79-87
[15] MAHMOOD K, LI X, CHAUDHRY S A, et al. Pairing based anonymous and secure key agreement protocol for smart grid edge computing infrastructure[J]. Future Generation Computer Systems, 2018, 88: 491-500.
[16] 钱斌, 蔡梓文, 肖勇, 等. 基于边缘计算的电表计量系统数据协同检测方案[J]. 中国电力, 2019, 52(11): 145-152
QIAN Bin, CAI Ziwen, XIAO Yong, et al. Data collaborative detection scheme of electric metering system based on edge computing[J]. Electric Power, 2019, 52(11): 145-152
[17] QURESHI N M F, SIDDIQUI I F, UNAR M A, et al. An aggregate MapReduce data block placement strategy for wireless IoT edge nodes in smart grid[J]. Wireless Personal Communications, 2019, 106(4): 2225-2236.
[18] HOWARD A, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17) [2019-08-08]. https://arxiv.org/abs/1704.04861.
[19] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-14) [2019-08-08]. https://arxiv.org/abs/1409.1556.
[20] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.
[21] 张慧. 深度学习中优化算法的研究与改进[D]. 北京: 北京邮电大学, 2018.
ZHANG Hui. Research and improvement of optimization algorithms in deep learning[D]. Beijing: Beijing University of Posts and Telecom, 2018.

基于边缘计算与深度学习的输电设备异物检测方法

Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning

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基于边缘计算与深度学习的输电设备异物检测方法

Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning

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