A method for generating tire defect images based on improved DCGAN

Abstract

An improved DCGAN tire defect image generation model was proposed to solve the problems of poor image quality, unstable model frame and slow model convergence in the data expansion method of deep convolutional generative adversarial network. The residual network and attention mechanism were embedded in DCGAN model to improve the feature extraction ability of the model. At the same time, the DCGAN loss function JS divergence was abandoned and Wasserstein distance with gradient penalty term was used to improve the stability of model training. The experimental results show that the quality of tire defect images generated by this model is better than that generated by DCGAN, WGAN, CGAN and SAGAN,with an average FID value of 11628 and a minimum FID value of 8494. The proposed model can stably generate tire defect images with better quality, which provides an effective way for expanding tire defect sample dataset and alleviates the problem of small sample in the development of deep learning in the field of defect detection.

Keywords

• computer neural network; generative adversarial network; residual network; attention mechanism; data expansion