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

doi:10.11930/j.issn.1004-9649.201910011

Abstract

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)

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201910011

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

References

[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.