For defect detection in tire X-ray images generated by adversarial networks,during the training phase,the generator may lose some image features and find it difficult to determine the potential spatial dimensions of the samples,resulting in unnecessary image feature reconstruction.In order to solve these problems,a sample resampling generation adversarial network(SRGAN)was con-structed.The generator uses VQ-VAE as the basic framework and utilizes the Attention Feature Fusion(AFF)module to build a new hop layer.The conversion loss function LVQ was added to the SRGAN generator.Finally,the self-made tire X-ray image dataset was used to train and test SRGAN and the proposed partially generated adversarial network model,and the obtained AUC values were com-pared,further proving that SRGAN has better image defect detection ability.
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