Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation-Reference-Cited by-同舟云学术

Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation

Published:2023-09 Issue:9 Volume:53 Page:5784-5795
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Zhang Zhaoxiang¹^ORCID,Zhang Limao²^ORCID

Affiliation:

1. Department Unmanned System Research Center, Northwestern Polytechnical University, Xi'an, China

2. School of Civil and Hydraulic Engineering, National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan, Hubei, China

Funder

National Natural Science Foundation of China

Outstanding Youth Fund of Hubei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221021/10223304/10129260.pdf?arnumber=10129260

Reference53 articles.

1. Deep convolutional neural networks for detection of rail surface defects

2. Automatic Visual Detection System of Railway Surface Defects With Curvature Filter and Improved Gaussian Mixture Model

3. Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

4. Detection of rail surface defects based on CNN image recognition and classification;shang;Proc 20th Int Conf Adv Commun Technol (ICACT),2018

5. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

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