计算机工程与应用2018，Vol.54Issue(10)：242-248,266,8.DOI:10.3778/j.issn.1002-8331.1612-0419
基于卷积神经网络的城管案件图像分类方法
Method of urban management cases'image classification based on convolutional neural network
摘要
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.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/ReLUKey words
urban management/image classification/Convolution Neural Network(CNN)/Zero-phase Component Analysis (ZCA)-whitening/dropout/ReLU分类
信息技术与安全科学引用本文复制引用
杨浩,李灵巧,杨辉华,刘振丙,潘细朋..基于卷积神经网络的城管案件图像分类方法[J].计算机工程与应用,2018,54(10):242-248,266,8.基金项目
广西重点研发计划项目(桂科AB16380293) （桂科AB16380293）
国家自然科学基金资助项目(No.21365008,No.61562013). （No.21365008,No.61562013）
