The traditional Faster r-cnn positioning algorithm uses RoiPooling, which is interpolated by the nearest neighbor interpolation algorithm. The recognition effect of small defects is not good. This article will improve it to RoiAlign using bilinear interpolation algorithm, which improves tire abnormality detection. Accuracy. Traditional tire defect sample detection faces the problem of difficult feature extraction. In this paper, the RSDC-Net (resnet and densenet converged network) network model built by fusing the two network models of ResNet and DenseNet has improved the generalization and perception capabilities of the network. , Enhance the feature extraction ability, and apply the network to the interpretability research of deep learning, and realize the visualization of deep learning. At present, there is still a big gap in the research field of neuron classification. Therefore, in order to carry out the research on the neuron classification of the latent layer according to the image results of the sensitive area, this paper designs a double convolution threshold recurrent neural network as a network model to complete the neuron classification , The network model performed best in the four model comparison experiments.