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Volume 14
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10.3390/app14177907
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Article

VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation

by
Gui Yu
1,2,3,4,
Xinglin Zhou
2,3,4,* and
Xiaolan Chen
1
1
School of Mechatronic and Intelligent Manufacturing, Huanggang Normal University, Huanggang 438000, China
2
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
3
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
4
School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 7907; https://doi.org/10.3390/app14177907
Submission received: 17 July 2024 / Revised: 24 August 2024 / Accepted: 3 September 2024 / Published: 5 September 2024
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Abstract

Addressing the challenge of limited samples arising from the difficulty and high cost of pavement crack, image collecting and labeling, along with the inadequate ability of traditional data augmentation methods to enhance sample feature space, we propose VDCrackGAN, a generative adversarial network combining VAE and DCGAN, specifically tailored for pavement crack data augmentation. Furthermore, spectral normalization is incorporated to enhance the stability of network training, and the self-attention mechanism Swin Transformer is integrated into the network to further improve the quality of crack generation. Experimental outcomes reveal that in comparison to the baseline DCGAN, VDCrackGAN achieves notable improvements of 13.6% and 26.4% in the Inception Score (IS) and Fréchet Inception Distance (FID) metrics, respectively.
Keywords: data augmentation; generative adversarial network; Swin transformer; crack detection data augmentation; generative adversarial network; Swin transformer; crack detection

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MDPI and ACS Style

Yu, G.; Zhou, X.; Chen, X. VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation. Appl. Sci. 2024, 14, 7907. https://doi.org/10.3390/app14177907

AMA Style

Yu G, Zhou X, Chen X. VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation. Applied Sciences. 2024; 14(17):7907. https://doi.org/10.3390/app14177907

Chicago/Turabian Style

Yu, Gui, Xinglin Zhou, and Xiaolan Chen. 2024. "VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation" Applied Sciences 14, no. 17: 7907. https://doi.org/10.3390/app14177907

APA Style

Yu, G., Zhou, X., & Chen, X. (2024). VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation. Applied Sciences, 14(17), 7907. https://doi.org/10.3390/app14177907

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