交通信息与安全2017，Vol.35Issue(5)：37-44,8.DOI:10.3963/j.issn.1674-4861.2017.05.005
基于深度学习的多角度车辆动态检测方法
A Multi-aspect Method for Vehicle Dynamic Detection Based On Deep Learning
摘要
Abstract
In order to address the problems of dynamic target detection rate is low due to excessive interference of background areas and fast moving speed of detected targets in complex scenes,this article proposes a multi-aspect method for vehicle dynamic detection based on deep learning.The traditional deep learning algorithm is improved by using convolutional neural network with a multiplayer perceptron (MLP-CNN).The kernel of this improved method is first to apply the fast candidate region extraction algorithm to find the regions where vehicles may exist,then to utilize a deep convolutional neural network (CNN) to extract features of candidate region,and to use an enhanced convolutional layer with multilayer perceptron (MLP) to further abstract optimal features for each layer.The Support vector machine (SVM) is finally used to classify CNN features of backgrounds.The results show that the proposed method can deal with part occlusion or fast motion objects.With a recognition accuracy of 87.9％ and elapsed time of 225 ms,it is more efficient than other traditional methods.关键词
智能交通/车辆检测/深度学习/卷积神经网/微型神经网Key words
intelligent transportation/vehicle detection/deep learning/convolutional neural network/multilayer perceptron分类
交通工程引用本文复制引用
李浩,张运胜,连捷,李泽萍..基于深度学习的多角度车辆动态检测方法[J].交通信息与安全,2017,35(5):37-44,8.基金项目
国家自然基金项目(71563045)、西安市科技计划项目(CXY1531WL25)资助 （71563045）
