Work

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

Year: 2024

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app14177907

Authors: Gui Yu, Xinglin Zhou, Xiaolan Chen

Institutions: Huanggang Normal University, Wuhan University of Science and Technology

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728