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

下载

页码：1166 / 1170

页数：5

共 50 条

[1] Skin lesion classification based on the VGG-16 fusion residual structure
Yan, Pu
Wang, Gang
Chen, Jie
Tang, Qingwei
Xu, Heng
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023, 33 (01) : 53 - 68
[2] A Lightweight Model of VGG-16 for Remote Sensing Image Classification
Ye, Mu
Ruiwen, Ni
Chang, Zhang
He, Gong
Tianli, Hu
Shijun, Li
Yu, Sun
Tong, Zhang
Ying, Guo
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 (14) : 6916 - 6922
[3] A Transfer Learning Method based on VGG-16 Convolutional Neural Network for MI Classification
Li, Ming-Ai
Xu, Dong-Qin
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 5430 - 5435
[4] Glove detection system based on VGG-16 network
Jin, Miao
Chen, Xiwen
Lai, Guoshu
Guo, Zhiwei
Huang, Tianfu
Chen, Zhuo
Wang, Quan
Fu, Jiniang
Nie, Gaoning
Zhang, Jun
2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020), 2020, : 172 - 175
[5] Simplifying VGG-16 for Plant Species Identification
Campos, Juan
Yee, Arturo
Vega, Ines F.
IEEE LATIN AMERICA TRANSACTIONS, 2022, 20 (11) : 2330 - 2338
[6] Retinal Arteriosclerosis Detection Based on Improved VGG-16 Network
Wu, Jun
Li, Di
Xiao, Zhitao
Geng, Lei
Zhang, Fang
Liu, Yanbei
Wang, Wen
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2019, 9 (09) : 1892 - 1899
[7] Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture
Sriram G.
Babu T.R.G.
Praveena R.
Anand J.V.
MCB Molecular and Cellular Biomechanics, 2022, 19 (01): : 29 - 40
[8] Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier
Bhagat M.
Kumar D.
Kumar S.
International Journal of Information Technology, 2023, 15 (1) : 465 - 475
[9] Plant Disease Detection using AI based VGG-16 Model
Alatawi, Anwar Abdullah
Alomani, Shand Maadi
Alhawiti, Najd Ibrahim
Ayaz, Muhammad
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (04) : 718 - 727
[10] Robustness Analysis for VGG-16 Model in Image Classification of Post-Hurricane Buildings
Li, Haoyang
Wang, Xinyi
2021 2ND INTERNATIONAL CONFERENCE ON BIG DATA & ARTIFICIAL INTELLIGENCE & SOFTWARE ENGINEERING (ICBASE 2021), 2021, : 401 - 407

← 1 2 3 4 5 →