基于广义结构元的交通限速标志检测与识别研究

发布时间：2018-03-30 19:44

本文选题：智能交通　切入点：数字图像处理　出处：《重庆交通大学》2015年硕士论文

【摘要】：智能交通系统(ITS)以交通运输、服务控制和车辆制造为研究对象,以解决交通拥堵、增强车辆安全性、提高汽车智能化为研究目的。道路交通标志识别(TSR)是智能交通系统中对于服务控制研究的重要组成部分,其研究主要需要电子信息、视频处理、数字图像处理、人工智能和模式识别等技术手段。目前,交通限速标志检测识别方法普遍存在识别正确率不理想,识别速度不够快,对于残损交通限速标志难以识别等问题。为此,本文选择对交通限速标志的检测识别方法进行研究,采用广义结构元进行标志检测,设计新的模板匹配识别算法,拟解决识别率不高、识别速度慢和对于残损交通限速标志的识别难等问题。主要研究内容如下:(1)图像预处理。比较RGB和HSI空间对于交通限速标志检测的优缺点,选用HSI空间对交通限速标志进行处理。在此空间中对图像进行直方图增强,维纳滤波,几何变换等预处理。(2)广义结构元滤波。引入广义结构元思想进入交通限速标志检测识别中,构建三个广义结构元对三种交通限速标志进行扩展形态学滤波。根据构建的广义结构元,对待识别图像分别进行红色圆形图案、蓝色圆形图案、黑色圆形图案检测。(3)数字提取。根据检测到的感兴趣区域,在此区域中通过数字提取技术,将交通限速标志的关键数字提取出来并归一化。(4)标志识别。得到归一化后的数字,根据三种交通限速标志,分别采用对应的基于16个特征分量、正确匹配数为10且对单个特征分量具有阈值限制的限速标志识别模板进行识别。(5)实验分析。采用C#语言开发实验软件,对本文算法理论进行验证。通过实验得到,本文设计的可识别残损交通限速标志的算法在对一般限速标志和残损限速标志的识别正确率上分别为91.67%和89.83%,综合识别正确率为90.80%。本文的主要创新点如下:(1)提出了利用广义结构元进行交通限速标志检测识别的方法。(2)提出了残损交通限速标志检测识别算法。
[Abstract]:Intelligent Transportation system (its) focuses on transportation, service control and vehicle manufacturing, which aims at solving traffic congestion, enhancing vehicle safety and improving vehicle intelligence.Road traffic sign recognition (TSRs) is an important part of the research on service control in intelligent transportation system. The research of TSRs mainly needs electronic information, video processing, digital image processing, artificial intelligence and pattern recognition.At present, traffic speed limit sign detection and recognition methods generally have some problems, such as the recognition accuracy is not ideal, the recognition speed is not fast enough, and it is difficult to recognize the damaged traffic speed limit sign, and so on.Therefore, this paper chooses to study the detection and recognition method of traffic speed limit sign, adopts generalized structure element to detect the sign, designs a new template matching recognition algorithm, and proposes to solve the problem that the recognition rate is not high.The recognition speed is slow and it is difficult to identify the speed limit sign of damaged traffic.The main contents are as follows: 1) Image preprocessing.This paper compares the advantages and disadvantages of RGB and HSI space in detecting traffic speed limit signs, and selects HSI space to deal with traffic speed limit signs.In this space, histogram enhancement, Wiener filter, geometric transformation and other preprocessing.The idea of generalized structure element is introduced into the detection and identification of traffic speed limit sign, and three generalized structure elements are constructed to carry out extended morphological filtering for three traffic speed limit signs.According to the generalized structure element, the recognition image is extracted from red circular pattern, blue circular pattern and black circular pattern detection.According to the detected region of interest, the key numbers of traffic speed limit signs are extracted and normalized.The normalized number is obtained. According to the three traffic speed limit signs, the corresponding recognition template based on 16 feature components, with a correct matching number of 10 and a threshold limit for a single feature component, is used to identify the speed limit signs.The experimental software is developed with C # language, and the algorithm theory of this paper is verified.The experimental results show that the algorithm designed in this paper is 91.67% and 89.83% respectively for the recognition of the general speed limit sign and the damaged speed limit sign, and the comprehensive recognition accuracy rate is 90.80%.The main innovations of this paper are as follows: (1) A method of detecting and recognizing traffic speed limit signs using generalized structure elements is proposed.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;TP391.41

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