Insulator visual non-conformity detection in overhead power distribution lines using deep learning-Reference-Cited by-同舟云学术

Insulator visual non-conformity detection in overhead power distribution lines using deep learning

Published:2019-09 Issue: Volume:78 Page:343-355
ISSN:0045-7906
Container-title:Computers & Electrical Engineering
language:en
Short-container-title:Computers & Electrical Engineering

Author:

Prates Ricardo M.,Cruz Ricardo,Marotta André P.,Ramos Rodrigo P.,Simas Filho Eduardo F.,Cardoso Jaime S.

Funder

CAPES

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science,Control and Systems Engineering

Reference25 articles.

1. Underground energy distribution networks: current situation and assessment of the need to improve the associated regulation;National Agency of Electric Energy,2014

2. Insulator detection in aerial images for transmission line inspection using single shot multibox detector;Miao;IEEE Access,2019

3. Power line robotic device for overhead line inspection and maintenance;Miller;Ind Rob,2017

4. Deep learning for smart manufacturing: methods and applications;Wang;J Manuf Syst,2018

5. Artificial intelligence in healthcare;Yu;Nat Biomed Eng,2018

Cited by 48 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence approaches for accurate assessment of insulator cleanliness in high-voltage electrical systems;Electrical Engineering;2024-08-27

2. ID-Det: Insulator Burst Defect Detection from UAV Inspection Imagery of Power Transmission Facilities;Drones;2024-07-05

3. TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators;Multimedia Tools and Applications;2024-03-21

4. Detection of Potential Faults in the Electricity Distribution Network Using Unmanned Aerial Vehicles and Thermal Cameras Through Deep Learning Methods;Electric Power Components and Systems;2024-03-11

5. Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid;Sensors;2024-02-08