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

Abstract

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.