A Novel Data Augmentation Method for Improved Visual Crack Detection Using Generative Adversarial Networks-Reference-Cited by-同舟云学术

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A Novel Data Augmentation Method for Improved Visual Crack Detection Using Generative Adversarial Networks

Published:2023 Issue: Volume:11 Page:22051-22059
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Branikas Efstathios¹^ORCID,Murray Paul¹^ORCID,West Graeme¹^ORCID

Affiliation:

1. Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10058512.pdf?arnumber=10058512

Reference28 articles.

1. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

2. Image-to-Image Translation with Conditional Adversarial Networks

3. Application of improved least-square generative adversarial networks for rail crack detection by AE technique

4. A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets

5. Generative adversarial networks

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