【作者】 张艳茹；

【导师】 李柏年；

【作者基本信息】 兰州大学 ， 电路与系统， 2013， 硕士

【摘要】 随着科技的发展和人们生活水平的不断提高,公路上各种车辆的数量与日俱增,这给人们的生活带来了极大的便利,同时交通事故问题也越来越严重。引发交通事故的因素为驾驶员错误操作、路况不好、天气影响等。据统计80%的交通事故归咎于驾驶员误操作。为了降低交通事故率,道路交通标志识别系统TSR (Traffic Signs Recognition)应运而生。道路交通标志识别系统识别交通标志并反馈给驾驶员可以使驾驶员提前了解路况及其变化并及时做出正确的反应措施,从而尽量减少交通事故的发生。在道路交通标志中,常见的是警告、指示和禁令交通标志,每类交通标志都由特定颜色和形状的图案、文字构成。当外界光照强度发生变化、天气变化,交通标志牌倾斜,采集到的交通标志存在尺度变化都会影响道路交通标志识别系统的识别效果。所以,需要使识别系统具有旋转、尺度不变性。另外,在图像采集过程中容易混入噪声,所以系统需要具有抗噪性。道路交通标志识别系统主要包括交通标志采集、预处理、检测和识别。采集并识别不同光照条件下的道路交通标志可以验证道路交通识别系统的稳定性。为了降低光照强度对道路交通标志图像带来的影响,本文应用HSI彩色空间图像增强。在道路交通标志检测过程中,根据交通标志的颜色特性,在Lab彩色空间中应用Mean shift图像分割算法,从而将道路交通标志从自然场景中分离出来。对数极坐标变换(LPT)与离散傅里叶变换(DFT)相结合的方法(LPT-DFT)具有旋转和尺度不变性。Contourlet变换和双数复小波变换都具有良好的多分辨率、多方向性并常应用于纹理特征提取。本文提出LPT-DFT分别与Contourlet变换、双树复小波变换(DT-CWT)结合的方法并应用Brodatz纹理图像库和交通标志图像库验证了这两种方法具有良好的旋转不变性、尺度不变性和抗噪性。另外,本文提出基于颜色直方图和纹理相结合的方法进行特征提取并通过应用模板匹配法对交通标志分类识别,通过实验进一步证明这两种方法能够有效的实现交通标志分类识别。更多还原

【Abstract】 With the development of technology and the increased living standards of the people, the number of private car on the highway is growing with each passing day. While the development of transportation has brought great convenience for people, at the same time the problems caused by traffic accidents become more and more serious. The factors that cause the traffic accident include the drivers’error, the weather conditions and so on. According to statistics,80%of the traffic accidents result from the misoperation of driver. In order to reduce the rate of traffic accidents, Traffic Signs Recognition System emerges. The traffic signs recognition system can recognize the traffic sign and then give the driver feedback that allows the driver timely response measures to correct under the changes of the road conditions so as to reduce the traffic accident.The most common types of the traffic signs are warning signs, the directional signs and the prohibition signs. Each type of traffic sign is composed of patterns and characters which have certain colors and shapes. When the illumination intensity or the weather changes, the traffic signs tilt or the collected traffic signs have scale change, they all could affect the recognition effect of the traffic sign recognition system. So the traffic signs recognition system should be of rotation, scale invariance. In addition, the image you grabbed easily contains noise signals, so the system needs to have anti-noise property. In the process of processing image, we use image enhance in the HSI Color Space to reduce the effect of the changes of illumination intensity.Road traffic sign recognition system mainly includes road traffic sign collecting, image preprocessing, image detecting and recognition. By Collecting and recognizing the road traffic signs in different light conditions can verify the stability of the traffic sign recognition system. In order to reduce impacts on the image of road traffic sign from illumination intensity, this paper applied the HSI color space of image enhancement, by the processing of luminance and saturation component so that the image for subsequent processing. In the process road traffic sign, according to the color characteristics of traffic signs, the application of Mean shift image in Lab color space segmentation algorithm, which will be road traffic signs from natural scenes. The combination of log-polar transform and Discrete Fourier Transform (LPT-DFT) has the prosperities of scale and rotation invariance. Contourlet transform and Dual-Tree Complex Wavelet Transform (DT-CWT) possess good multi-resolution and multi-direction, and then they are usually used to extract the texture feature vector. In this paper the method combined by the transformations from LPT-DFT to Contourlet transform and DT-CWT respectively and its applications to Brodatz texture image database verify the advantages, such as scale invariance, rotation invariance and anti-noise property. In addition,the method of combining two kinds of feature extraction based on texture and feature extraction based on color histogram method is used, and then template matching is applied to recognize road traffic sign. The experiments results demonstrate that the methods can effectively recognize the road traffic signs.更多还原