A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection
Authors
Keywords
-
Journal
Applied Sciences-Basel
Volume 9, Issue 10, Pages 2009
Publisher
MDPI AG
Online
2019-05-16
DOI
10.3390/app9102009
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection
- (2019) Jiang Bian et al. International Journal of Advanced Robotic Systems
- Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features
- (2019) Haiyan Cheng et al. Energies
- Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
- (2018) Junwei Han et al. IEEE SIGNAL PROCESSING MAGAZINE
- Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
- (2018) Van Nhan Nguyen et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Detection of the power lines in UAV remote sensed images using spectral-spatial methods
- (2018) Rishav Bhola et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance
- (2018) Xuezhi Xiang et al. SENSORS
- Insulator Fault Detection Based on Spatial Morphological Features of Aerial Images
- (2018) Yongjie Zhai et al. IEEE Access
- A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images
- (2018) Xiaojin Gong et al. IEEE Access
- A Contactless Insulator Contamination Levels Detecting Method Based on Infrared Images Features and RBFNN
- (2018) Hongying He et al. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
- Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning
- (2018) Gaoqiang Kang et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks
- (2018) Xian Tao et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Acoustic Fault Detection Technique for High-Power Insulators
- (2017) Kyu-Chil Park et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Real-time transmission tower detection from video based on a feature descriptor
- (2017) Alexander Cerón et al. IET Computer Vision
- Automatic identification and location technology of glass insulator self-shattering
- (2017) Xinbo Huang et al. JOURNAL OF ELECTRONIC IMAGING
- Automatic Power Line Inspection Using UAV Images
- (2017) Yong Zhang et al. Remote Sensing
- Aggregating Deep Convolutional Feature Maps for Insulator Detection in Infrared Images
- (2017) Zhenbing Zhao et al. IEEE Access
- Fault detection of insulator based on saliency and adaptive morphology
- (2016) Yongjie Zhai et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images
- (2015) Shenglong Liao et al. IEEE Geoscience and Remote Sensing Letters
- Localization of multiple insulators by orientation angle detection and binary shape prior knowledge
- (2015) Zhenbing Zhao et al. IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION
- The recognition and localization of insulators adopting SURF and IFS based on correlation coefficient
- (2014) Zhenbing Zhao et al. OPTIK
- An Active Contour Model Based on Texture Distribution for Extracting Inhomogeneous Insulators From Aerial Images
- (2013) Qinggang Wu et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started