基于卷积神经网络的交通物体识别系统的设计与实现
发布时间：2019-06-04 11:04
【摘要】：随之我国经济社会的不断向前发展,城市中的人口以及汽车保有量不断增多,城市交通问题日益突出。智能交通系统的中存储着大量高清图片,这些交通图像数据中包含着大量有价值信息,是警察部门破案、刑侦以及解决交通问题的重要工具。但是,传统的智能交通系统中,缺乏有效的方案来提取图像数据中的信息,特别是对于交通场景下的行人、车辆等物体识别任务,目前仍是以人工识别方式为主,浪费人力警力,慢速低效。针对交通系统每天产生的海量图片与智能交通系统物体识别能力不足之间的矛盾,我们设计实现了面向交通图像数据的快速物体识别系统,旨在利用先进的计算机技术,实现快速的交通图像物体识别,提取有效信息,辅助警方工作。在调研过程中,发现卷积神经网络作为近年来计算机视觉领域的前沿技术,在物体定位、物体识别、物体分类等多个领域都取得了很好效果,受到了学术界与工业界的高度关注。因此,我们选择利用卷积神经网络来进行物体识别任务,并且利用回归思想结合卷积神经网络来减少中间操作,实现快速的物体识别。而且,考虑到GPU在图像数据处理中的强大能力,在某些环节采用CUDA编程方式,进一步加快处理速度。基于卷积神经网络的快速物体识别系统,采用模块化设计方案,各个模块均选择成熟技术进行开发。采用卷积神经网络作为物体识别技术,并利用GPU的高计算能力进一步加速识别过程,主要采用C/C++进行开发,在卷积网络模块会引入CUDA编程来加快运行速度,实现快速高效识别。数据接入采用成熟的Inotify技术,采用主动方式去获得相关数据,进一步减少延迟。与其他相关系统的通信采用网络通信方式,避免了因为系统异构造成的通信障碍,而且能够配合交通部门已经建立的大数据存储系统共同使用。本系统针对交通物体识别这一功能需求,利用回归的思想设计了相关的卷积神经网络模型,填补了交通警察部门的智能交通系统在图像相关业务方面的空缺,使得用户可以更加快速高效地利用交通图像数据,进一步加强了警方在交通领域的管理能力,对于实现现代化的交通管理具有重要意义。
[Abstract]:Along with the development of our country's economy and society, the population in the city and the number of cars in the city are increasing, and the problem of urban traffic is becoming more and more serious. The intelligent transportation system stores a large number of high-definition pictures, which contain a large amount of valuable information, which is an important tool for the police department to break the case, criminal investigation and solve the traffic problem. However, in the traditional intelligent traffic system, there is a lack of effective scheme to extract the information in the image data, especially for pedestrian, vehicle and other objects in the traffic scene. Aiming at the contradiction between the mass picture produced by the traffic system and the insufficient recognition capability of the intelligent traffic system object every day, the rapid object recognition system aiming at the traffic image data is designed, and the aim of the invention is to realize the rapid traffic image object recognition by using the advanced computer technology, The effective information is extracted and the police are assisted to work. In the course of investigation, it is found that the convolution neural network has achieved good results in many fields such as object location, object recognition, object classification and so on, and has been highly concerned by the academic and industry. Therefore, we choose to use the convolution neural network to carry on the object recognition task, and use the regression idea to combine the convolution neural network to reduce the intermediate operation and realize the fast object recognition. In addition, considering the powerful ability of the GPU in the image data processing, the CUDA programming mode is adopted in some links, and the processing speed is further accelerated. The rapid object recognition system based on the convolution neural network adopts the modular design scheme, and each module selects the mature technology for development. In this paper, the convolution neural network is used as the object recognition technology, and the high computing power of the GPU is used to further accelerate the identification process, and the C/ C ++ is mainly used for development, and the convolution network module can introduce the CUDA programming to speed up the running speed and realize the fast and high-efficiency identification. Data access adopts the mature Intify technology, and the active mode is adopted to obtain the relevant data, and the delay is further reduced. The communication with other relevant systems adopts the network communication mode, so that the communication barrier caused by the heterogeneous system is avoided, and the large-data storage system which has been established by the traffic department can be used for common use. in that system, the function requirement of the traffic object is identify, the relevant convolution neural network model is designed by using the thought of the regression, and the vacancy of the intelligent traffic system in the traffic police department in the image-related business is filled, So that the user can use the traffic image data more quickly and efficiently, and further strengthens the management capability of the police in the traffic field, and is of great significance for realizing the modern traffic management.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.52;TP183
本文编号：2492678
[Abstract]:Along with the development of our country's economy and society, the population in the city and the number of cars in the city are increasing, and the problem of urban traffic is becoming more and more serious. The intelligent transportation system stores a large number of high-definition pictures, which contain a large amount of valuable information, which is an important tool for the police department to break the case, criminal investigation and solve the traffic problem. However, in the traditional intelligent traffic system, there is a lack of effective scheme to extract the information in the image data, especially for pedestrian, vehicle and other objects in the traffic scene. Aiming at the contradiction between the mass picture produced by the traffic system and the insufficient recognition capability of the intelligent traffic system object every day, the rapid object recognition system aiming at the traffic image data is designed, and the aim of the invention is to realize the rapid traffic image object recognition by using the advanced computer technology, The effective information is extracted and the police are assisted to work. In the course of investigation, it is found that the convolution neural network has achieved good results in many fields such as object location, object recognition, object classification and so on, and has been highly concerned by the academic and industry. Therefore, we choose to use the convolution neural network to carry on the object recognition task, and use the regression idea to combine the convolution neural network to reduce the intermediate operation and realize the fast object recognition. In addition, considering the powerful ability of the GPU in the image data processing, the CUDA programming mode is adopted in some links, and the processing speed is further accelerated. The rapid object recognition system based on the convolution neural network adopts the modular design scheme, and each module selects the mature technology for development. In this paper, the convolution neural network is used as the object recognition technology, and the high computing power of the GPU is used to further accelerate the identification process, and the C/ C ++ is mainly used for development, and the convolution network module can introduce the CUDA programming to speed up the running speed and realize the fast and high-efficiency identification. Data access adopts the mature Intify technology, and the active mode is adopted to obtain the relevant data, and the delay is further reduced. The communication with other relevant systems adopts the network communication mode, so that the communication barrier caused by the heterogeneous system is avoided, and the large-data storage system which has been established by the traffic department can be used for common use. in that system, the function requirement of the traffic object is identify, the relevant convolution neural network model is designed by using the thought of the regression, and the vacancy of the intelligent traffic system in the traffic police department in the image-related business is filled, So that the user can use the traffic image data more quickly and efficiently, and further strengthens the management capability of the police in the traffic field, and is of great significance for realizing the modern traffic management.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.52;TP183
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