Design and Application of Transmission Line Intelligent Monitoring System

This paper presents a design method of intelligent monitoring system for transmission lines based on artificial intelligence technology. In this design method, a low-power artificial intelligence chip - LieYing A101 is used to design an intelligent recognition module to realize real-time target reco...

Full description

Bibliographic Details
Main Authors: Xing Zengqiang, Cui WenpengCui, Liu Rui, Zheng Zhe
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/45/e3sconf_iceeb2020_01063.pdf
id doaj-478e147c736a44b190f1a50d39aad92b
record_format Article
spelling doaj-478e147c736a44b190f1a50d39aad92b2021-04-02T14:40:03ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011850106310.1051/e3sconf/202018501063e3sconf_iceeb2020_01063Design and Application of Transmission Line Intelligent Monitoring SystemXing Zengqiang0Cui WenpengCui1Liu Rui2Zheng Zhe3Beijing Smartchip Microelectronics Technology Company LimitedBeijing Smartchip Microelectronics Technology Company LimitedBeijing Smartchip Microelectronics Technology Company LimitedBeijing Smartchip Microelectronics Technology Company LimitedThis paper presents a design method of intelligent monitoring system for transmission lines based on artificial intelligence technology. In this design method, a low-power artificial intelligence chip - LieYing A101 is used to design an intelligent recognition module to realize real-time target recognition on a terminal device. In order to solve the problem that the original image and the input image resolution of the intelligent recognition module do not match, this paper uses a sliding window and convolutional neural network design method, which solves the image resolution mismatch problem and improves the recognition accuracy. Finally, for the problem of excessive network model size, feature channel weight pruning and 8-bit quantization methods are used to compress the network model to less than 10M, and the recognition accuracy is not sharply reduced. After the test set test and actual scene use, the external force destruction target recognition accuracy of the transmission line channel is high; this meets the application needs of customers.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/45/e3sconf_iceeb2020_01063.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Xing Zengqiang
Cui WenpengCui
Liu Rui
Zheng Zhe
spellingShingle Xing Zengqiang
Cui WenpengCui
Liu Rui
Zheng Zhe
Design and Application of Transmission Line Intelligent Monitoring System
E3S Web of Conferences
author_facet Xing Zengqiang
Cui WenpengCui
Liu Rui
Zheng Zhe
author_sort Xing Zengqiang
title Design and Application of Transmission Line Intelligent Monitoring System
title_short Design and Application of Transmission Line Intelligent Monitoring System
title_full Design and Application of Transmission Line Intelligent Monitoring System
title_fullStr Design and Application of Transmission Line Intelligent Monitoring System
title_full_unstemmed Design and Application of Transmission Line Intelligent Monitoring System
title_sort design and application of transmission line intelligent monitoring system
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2020-01-01
description This paper presents a design method of intelligent monitoring system for transmission lines based on artificial intelligence technology. In this design method, a low-power artificial intelligence chip - LieYing A101 is used to design an intelligent recognition module to realize real-time target recognition on a terminal device. In order to solve the problem that the original image and the input image resolution of the intelligent recognition module do not match, this paper uses a sliding window and convolutional neural network design method, which solves the image resolution mismatch problem and improves the recognition accuracy. Finally, for the problem of excessive network model size, feature channel weight pruning and 8-bit quantization methods are used to compress the network model to less than 10M, and the recognition accuracy is not sharply reduced. After the test set test and actual scene use, the external force destruction target recognition accuracy of the transmission line channel is high; this meets the application needs of customers.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/45/e3sconf_iceeb2020_01063.pdf
work_keys_str_mv AT xingzengqiang designandapplicationoftransmissionlineintelligentmonitoringsystem
AT cuiwenpengcui designandapplicationoftransmissionlineintelligentmonitoringsystem
AT liurui designandapplicationoftransmissionlineintelligentmonitoringsystem
AT zhengzhe designandapplicationoftransmissionlineintelligentmonitoringsystem
_version_ 1721561729881604096