Transmission Lines Scenes Classification Based on Optimized VGG-16

被引：0

作者：

Zhang, Qiuyan ^{[1
]}

Yang, Zhong ^{[2
]}

Jiang, Yuhong ^{[3
]}

Li, Hongchen ^{[2
]}

Han, Jiaming ^{[2
]}

Xu, Changliang ^{[2
]}

Xu, Hao ^{[2
]}

Xu, Xiangrong ^{[4
]}

机构：

[1] Guizhou Power Grid Co Ltd, Elect Power Res Inst, Guiyang 550000, Guizhou, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Key Lab, Minist Ind & Informat Technol, Nanjing 210016, Jiangsu, Peoples R China

[3] Nanjing Univ Aeronaut & Astronaut, Res Inst UAV, Nanjing 210016, Jiangsu, Peoples R China

[4] Anhui Univ Technol, Sch Mech Engn, Maanshan 243032, Anhui, Peoples R China

来源：

2019 9TH IEEE ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (IEEE-CYBER 2019) | 2019年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Insulators are important part of transmission lines. Traditionally, insulator-detection methods mainly relied on manual operation, which suffered from low efficiency and poor safety. Due to the rapid development of deep learning, convolutional neural networks(CNNs) have been widely applied in the field of image classification. However, traditional CNNs have poor performance in transmission lines scenes classification. We propose an optimized deep new network based on traditional CNNs. The experimental results show that the proposed optimization method can improve the accuracy of transmission lines scenes classification.

引用

页码：1166 / 1170

页数：5

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