Algorithm for Generating Tire Defect Images Based on RS-GAN

Aiming at the problems of poor image quality, unstable training process and slow convergence speed in the data expansion method for generating countermeasures network, this paper proposes a RS-GAN tire defect image generative model. Compared with traditional generative adversarial networks, RS-GAN integrates residual networks and attention mechanisms into an RSNet embedded in the adversarial network structure to improve the model’s feature extraction ability; at the same time, the loss function JS divergence of the traditional generation countermeasure network is replaced by Wasserstein distance with gradient penalty term to improve the stability of model training. The experimental results show that the FID value of tire defect images generated by the RS-GAN model can reach 86.75, which is superior to the images generated by DCGAN, WGAN, CGAN, and SAGAN. Moreover, it has achieved more competitive results on SSIM and PSNR. The RS-GAN model can stably generate high-quality tire defect images, providing an effective way to expand the tire defect dataset and alleviating the small sample problem faced by the development of deep learning in the field of defect detection.