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基于深度学习的交通视频检测及车型分类研究

发布时间:2018-11-18 13:10
【摘要】:随着汽车保有量的急剧增加,交通问题越来越突出。与此同时,在互联网大数据时代的背景下,深度学习获得了迅猛发展,给模式识别任务带来了巨大的变革,它还给许多领域提供了一种新的解决方案。因此,将深度学习应用到解决交通问题已经成为一种研究趋势。本文利用深度学习中的卷积神经网络方法来解决交通视频中的交通目标检测及车型分类问题,为智能交通系统提供技术支持从而缓解交通拥堵等问题。本文主要内容如下:首先介绍了深度学习的基本模型,主要分为深度置信网络、栈式自编码网络和卷积神经网络,主要重点研究了卷积神经网络的构成、卷积神经网络区别于传统神经网络的特点,以及卷积神经网络的训练机制。针对利用人工设计的学习特征进行交通目标检测时,会存在学习特征设计过程繁琐、适应范围受限制等问题,本文采用卷积神经网络来自动提取特征。以基于区域的卷积神经网络(RCNN)为基础,设计了交通视频检测方案,结合了Fast RCNN框架和RPN区域建议网络的优点。针对交通目标轮廓形状各异的特点,本文对交通视频检测网络中的共享卷积网络进行了改进,主要是加深了卷积网络的深度,从5层卷积加深到13层。在交通训练样本中取得了较好的效果,交通目标的平均检测率提升了超过3%。针对已有的车型分类手段只将车辆进行粗略分类,已经无法满足车联网对车辆信息需求的问题,本文采用深度残差神经网络对车型进行精细型分类,车辆品牌可达64种,车型可达281种。在设计车型分类网络的过程中,分析了常用图像分类卷积神经网络,并在两套数据集上进行了性能对比,最终选择了深度残差网络作为车型分类网络的主体框架。利用标准车型数据集CompCars对车型分类网络进行可学习参数微调,训练后的车型分类网络的前五准确率在CompCars数据集上可达97.3%,在Vehicle ID数据集上可达89.4%,验证了车型分类网络的有效性。最后,对本文设计的基于交通视频的检测网络和车型分类网络分别在图像和视频上进行了检验。检测网络能在晴天、黑夜、雨天和拥堵等不同状态获得较高的检测率,在有效视野中车辆检测率最高可达98.7%,并具有一定的鲁棒性。分类网络在基于视频产生的车辆图像测试集中,获得了最高达到88%的前五准确率。实验结果表明,本文所设计的检测网络和分类网络具有一定的实用价值。
[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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