Abstract:Aiming at the traditional methods based on machine learning artificial design features which can not accurately classify large-scale transformer images because transformers have many types and their images are complex,a transformer image recognition system is proposed based on deep learning,which directly performs "end-to-end" learning on the original image.In order to achieve accurate classification of transformer images,a transformer image recognition model based on improved VGG-16 convolutional neural network is proposed.Based on the VGG-16 convolutional neural network model,the fully connected layer is reconstructed.The original SoftMax classifier is replaced with the 3-label classifier to optimize the network structure.The weighting parameters of the shared VGG-16 model convolutional layer and down-sampling layer are learned by transfer.By building a transformer image training set and test set,the improved model training and the performance of the improved method are tested.The experimental results show that compared with the deep neural network and the convolutional neural network model,the improved VGG-16 has better effect,achieving a recognition error of 9.17%,and achieving accurate differentiation of the three transformers.