Identification of Electrical Equipment Based on Faster LSTM-CNN Network

Xiaoping Xiong; Shuang Xu; Wenliang Wu; Deran Tu; Jie Zhang; Zhi Wei

doi:10.1109/ICNSC48988.2020.9238109

Identification of Electrical Equipment Based on Faster LSTM-CNN Network

Xiaoping Xiong, Shuang Xu, Wenliang Wu, Deran Tu, Jie Zhang, Zhi Wei

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728168531
DOIs	https://doi.org/10.1109/ICNSC48988.2020.9238109
State	Published - Oct 30 2020
Externally published	Yes
Event	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 - Nanjing, China Duration: Oct 30 2020 → Nov 2 2020

Publication series

Name	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

Conference

Conference	2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
Country/Territory	China
City	Nanjing
Period	10/30/20 → 11/2/20

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Statistics, Probability and Uncertainty
Control and Optimization
Sensory Systems

Keywords

Faster R-CNN
LSTM
equipment recognition

Access to Document

10.1109/ICNSC48988.2020.9238109

Cite this

Xiong, X., Xu, S., Wu, W., Tu, D., Zhang, J., & Wei, Z. (2020). Identification of Electrical Equipment Based on Faster LSTM-CNN Network. In 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 Article 9238109 (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNSC48988.2020.9238109

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title = "Identification of Electrical Equipment Based on Faster LSTM-CNN Network",

abstract = "Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.",

keywords = "Faster R-CNN, LSTM, equipment recognition",

author = "Xiaoping Xiong and Shuang Xu and Wenliang Wu and Deran Tu and Jie Zhang and Zhi Wei",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 ; Conference date: 30-10-2020 Through 02-11-2020",

year = "2020",

month = oct,

day = "30",

doi = "10.1109/ICNSC48988.2020.9238109",

language = "English (US)",

series = "2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020",

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booktitle = "2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020",

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}

Xiong, X, Xu, S, Wu, W, Tu, D, Zhang, J & Wei, Z 2020, Identification of Electrical Equipment Based on Faster LSTM-CNN Network. in 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020., 9238109, 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020, Nanjing, China, 10/30/20. https://doi.org/10.1109/ICNSC48988.2020.9238109

Identification of Electrical Equipment Based on Faster LSTM-CNN Network. / Xiong, Xiaoping; Xu, Shuang; Wu, Wenliang et al.
2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9238109 (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Identification of Electrical Equipment Based on Faster LSTM-CNN Network

AU - Xiong, Xiaoping

AU - Xu, Shuang

AU - Wu, Wenliang

AU - Tu, Deran

AU - Zhang, Jie

AU - Wei, Zhi

PY - 2020/10/30

Y1 - 2020/10/30

N2 - Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.

AB - Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.

KW - Faster R-CNN

KW - LSTM

KW - equipment recognition

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U2 - 10.1109/ICNSC48988.2020.9238109

DO - 10.1109/ICNSC48988.2020.9238109

M3 - Conference contribution

AN - SCOPUS:85096352633

T3 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

BT - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

Y2 - 30 October 2020 through 2 November 2020

ER -

Xiong X, Xu S, Wu W, Tu D, Zhang J, Wei Z. Identification of Electrical Equipment Based on Faster LSTM-CNN Network. In 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9238109. (2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020). doi: 10.1109/ICNSC48988.2020.9238109

Identification of Electrical Equipment Based on Faster LSTM-CNN Network

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All Science Journal Classification (ASJC) codes

Keywords

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