Style-Controlled Image Synthesis of Concrete Damages Based on Fusion of Convolutional Encoder and Attention-Enhanced Conditional Generative Adversarial Network-Reference-Cited by-同舟云学术

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Style-Controlled Image Synthesis of Concrete Damages Based on Fusion of Convolutional Encoder and Attention-Enhanced Conditional Generative Adversarial Network

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Li Shengyuan¹^ORCID,Le Yushan²,Zhao Xuefeng³

Affiliation:

1. Lecture, School of Mechanics and Civil Engineering, China Univ. of Mining and Technology, Xuzhou 221116, China; Lecture, State Key Laboratory for Geomechanics and Deep Underground Engineering, China Univ. of Mining and Technology, Xuzhou 221116, China (corresponding author). ORCID: .

2. Master’s Student, School of Mechanics and Civil Engineering, China Univ. of Mining and Technology, Xuzhou 221116, China.

3. Professor, Engineering School of Civil Engineering, Dalian Univ. of Technology, Dalian 116024, China; Professor, State Key Laboratory of Coastal and Offshore, Dalian Univ. of Technology, Dalian 116024, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-6007

Reference50 articles.

1. Analysis of Edge-Detection Techniques for Crack Identification in Bridges

2. Image-based retrieval of concrete crack properties for bridge inspection

3. Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

4. Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation

5. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

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