关键词: 智慧城管, 图像分类, 卷积神经网络, 零相位分量分析（ZCA）白化, dropout, ReLU

Abstract: In this paper, an improved deep Convolution Neural Network（CNN） algorithm is proposed to classify the urban management cases’ images in city management system. CNN can extract the image features automatically. The procedure can be divided into three main stages. Initially, ZCA-whitening is used to reduce the correlation of the images. Then, an eight-layer CNN model is set up to classify the images processed by ZCA-whitening. In convolution layer, ReLU is employed to speed up the training phase, and dropout is used to prevent overfitting in pooling layer. Finally, BP algorithm is introduced to improve the robustness of algorithm in fine-tuning phase. The method achieves about 97.5% accuracy and the F1-Score is 0.98 on two classes’ images of traffic and environment. The performance exceeds the LSVM, SAE and the usual CNN algorithm. Also, it tests on four classes images of electric-bicycles, rubbish, cars and dustbins, the accuracy is 90.5%, F1-Score is 0.91, and the performance still exceeds the LSVM, SAE and the usual CNN algorithm.

Key words: urban management, image classification, Convolution Neural Network（CNN）, Zero-phase Component Analysis（ZCA）-whitening, dropout, ReLU