SceneNet: A Multifeature Joint Embedding Network With Complexity Assessment for Power Line Scene Classification-Reference-Cited by-同舟云学术

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SceneNet: A Multifeature Joint Embedding Network With Complexity Assessment for Power Line Scene Classification

Published:2023-12 Issue:6 Volume:59 Page:9094-9116
ISSN:0018-9251
Container-title:IEEE Transactions on Aerospace and Electronic Systems
language:
Short-container-title:IEEE Trans. Aerosp. Electron. Syst.

Author:

Zhao Le¹^ORCID,Yao Hongtai²^ORCID,Fan Yajun¹^ORCID,Ma Haihua¹^ORCID,Li Zhihui¹^ORCID,Tian Meng³^ORCID

Affiliation:

1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China

2. International Joint Laboratory for Cooperative Vehicular Networks of Henan, Henan University, Zhengzhou, China

3. School of Automation, Wuhan University of Technology, Wuhan, China

Funder

National Natural Science Foundation of China

Natural Science Project of Zhengzhou Science and Technology Bureau

Hubei Province Key R&D Program Funded Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Aerospace Engineering

Link

http://xplorestaging.ieee.org/ielx7/7/10353051/10246990.pdf?arnumber=10246990

Reference61 articles.

1. Robust power line extraction from aerial image using object-based Gaussian–Markov random field with gravity property parameters

2. A Graph Guided Convolutional Neural Network for Surface Defect Recognition

3. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

4. Adaptive Mini-Batch Gradient-Ascent-Based Localization for Indoor IoT Networks Under Rayleigh Fading Conditions

5. Power line detection from optical images

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1. An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection;Energy Informatics;2024-08-21

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