Urban management image classification approach based on deep learning

Based on the case images in the smart city management system, the advantage of deep learning is used to learn image features on its own, an improved deep convolutional neural network algorithm is proposed in this paper, and the algorithm is used to improve the smart city management system (hereinafter referred to as Smart City Management). These case images are quickly and accurately classified, the automatic classification of cases is completed in the city management system. ZCA (Zero-phase Component Analysis)-whitening is used to reduce the correlation between image data features, an eight-layer convolutional neural network model is built to classify the whitened images, and rectified linear unit (ReLU) is used in the convolutional layer to accelerate the training process, the dropout technology is used in the pooling layer, the algorithm is prevented from overfitting. Back Propagation (BP) algorithm is used for optimization in the network fine-tuning stage, the robustness of the algorithm is improved. Based on the above method, the two types of case images of road traffic and city appearance environment were subjected to two classification experiments. The accuracy has reached 97.5%, and the F1-Score has reached 0.98. The performance exceeded LSVM (Langrangian Support Vector Machine), SAE (Sparse autoencoder), and traditional CNN (Convolution Neural Network). At the same time, this method conducts four-classification experiments on four types of cases: electric vehicles, littering, illegal parking of motor vehicles, and mess around garbage bins. The accuracy is 90.5%, and the F1-Score is 0.91. The performance still exceeds LSVM, SAE and traditional CNN and other methods.

Automated summary

An improved deep convolutional neural network algorithm is proposed in this paper, which is used to achieve automatic classification of case images in the smart city management system. Through classification experiments, the algorithm has shown high efficiency and accuracy in categorizing different types of case images.