基于深度学习的交通视频检测及车型分类研究
[Abstract]:With the rapid increase of vehicle ownership, traffic problems become more and more prominent. At the same time, under the background of Internet big data era, in-depth learning has developed rapidly, which has brought a great change to the task of pattern recognition. It also provides a new solution in many fields. Therefore, the application of deep learning to solve traffic problems has become a research trend. In this paper, the convolution neural network method in depth learning is used to solve the traffic target detection and vehicle classification problems in traffic video, and to provide technical support for intelligent transportation system to alleviate traffic congestion and so on. The main contents of this paper are as follows: firstly, the basic model of deep learning is introduced, which is divided into three parts: depth confidence network, stack self-coding network and convolutional neural network. Convolutional neural networks are different from traditional neural networks and the training mechanism of convolutional neural networks. In order to solve the problem that the design process of learning features is cumbersome and the scope of adaptation is limited when using artificial design learning features to detect traffic targets, this paper uses convolution neural network to extract features automatically. Based on the area-based convolution neural network (RCNN), a traffic video detection scheme is designed, which combines the advantages of the Fast RCNN framework and the RPN regional recommendation network. In this paper, the shared convolution network in the traffic video detection network is improved, which mainly deepens the depth of the convolutional network, from five layers to 13 layers. Good results were obtained in traffic training samples, and the average detection rate of traffic targets increased by more than 3 percent. In view of the existing vehicle classification methods only rough classification of vehicles, can no longer meet the needs of vehicle information, this paper uses the depth residual neural network for fine classification of vehicle models, vehicle brands can reach 64, There are 281 types of models. In the course of designing the vehicle classification network, the neural network of image classification is analyzed, and the performance of the two sets of data sets is compared. Finally, the depth residual network is chosen as the main frame of the vehicle classification network. The model classification network can be fine-tuned by using the standard model data set (CompCars). The first five accuracy rates of the trained vehicle classification network can reach 97.3 on the CompCars data set and 89.4 on the Vehicle ID data set. The validity of vehicle classification network is verified. Finally, the detection network based on traffic video and vehicle classification network designed in this paper are tested on image and video, respectively. The detection network can obtain high detection rate in different states such as sunny, dark, rainy and congested. In the effective field of vision, the vehicle detection rate can be up to 98.775, and it is robust to a certain extent. In the vehicle image test set based on video, the first five accuracy rates of classification network are up to 88%. The experimental results show that the detection network and classification network designed in this paper have some practical value.
【学位授予单位】:哈尔滨工业大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TP391.41;TP183
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