Industrial Damage Sample Image Generation Method Based on Improved DCGAN

To address the limited availability of atmospheric natural environment test samples and the challenge of obtaining sufficient industrial damage sample data for deep learning, we propose IDS-GAN, a novel data generation method based on an improved DCGAN for small-sample industrial damage modeling. First, a Wasserstein distance loss function with a gradient penalty term is introduced as an adversarial loss to mitigate mode collapse and stabilize training. Furthermore, L1 loss and Structural Similarity Index Measure (SSIM) loss are incorporated to guide the generator’s training. Residual blocks are added to the generator to address the gradient vanishing problem commonly encountered in deep networks during high-resolution image generation. Experimental results demonstrate that, compared to a progressively trained DCGAN, the proposed IDS-GAN model generates industrial damage sample images with significantly higher quality and more distinct features. Specifically, the Fréchet Inception Distance (FID) score decreased by 18.6%, while the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) improved by 5.1% and 21.1%, respectively. These results indicate that IDS-GAN is an effective approach for generating industrial damage sample datasets, offering potential applications in relevant research fields.