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The Crack Generation Algorithm of Underwater Bridge Based on Improved Generative Adversarial Network

Auteur(s):




Médium: article de revue
Langue(s): anglais
Publié dans: Journal of Physics: Conference Series, , n. 1, v. 2694
Page(s): 012071
DOI: 10.1088/1742-6596/2694/1/012071
Abstrait:

To address the challenge of obtaining underwater bridge crack images for bridge defect detection, this paper proposes an enhanced CycleGAN algorithm based on a generative adversarial network. Within the encoder-decoder architecture, two key enhancements have been introduced. First, to prevent the loss of information at different scales during training, residual connections with 1x1 convolutional kernels have been added. Second, to prioritize useful feature information during model training, the CBAM attention mechanism has been incorporated. Experimental results demonstrate that the improved model significantly enhances performance, with a 29% increase in the FID index, as well as a 9% improvement in PSNR and a 7% improvement in SSIM.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1088/1742-6596/2694/1/012071.
  • Informations
    sur cette fiche
  • Reference-ID
    10777498
  • Publié(e) le:
    12.05.2024
  • Modifié(e) le:
    12.05.2024
 
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