导　　师： 唐琎

学科专业： 081203

授予学位： 硕士

作　　者： ;

机构地区： 中南大学

摘　　要： 交通标志的实时识别是通过安装在交通工具上的摄像机摄取户外自然场景中交通标志图像,输入计算机进行处理完成的,由于户外环境存在多种复杂因素的影响,它比非自然场景下的目标识别更具挑战性。本学位论文结合中国的实际,在交通标志图像库的建立、交通标志图像的预处理算法、交通标志图像的特征提取、交通标志的分类算法以及最后交通标志的判别方法等方面做了一些工作,本文研究内容和取得的主要成果如下： 1/)由于交通标志的具体形式各国不尽相同,并且国内关于交通标志识别的起步较晚,因此目前国内还没有公开发布的交通标志的数据库。本文对交通标志图像库的建立采取两种方式,第一种方式通过对标准图的图像预处理变换/(如旋转、扭曲、加入噪声/)来扩充交通标志图库的数量,提高识别的鲁棒性；第二种方式通过不同角度和距离实地拍摄交通标志,然后将拍摄的交通标志图像用实验室的交通标志检测算法把实景图中的交通标志截取出来组成交通标志图库。 2/)针对交通标志的分类问题和准备识别的交通标志,本文归纳总结了一种由粗到精的分层分类策略并将其应用到交通标志的识别上,根据基于颜色和形状的交通标志检测方法将要识别的交通标志按颜色和形状首先粗分类为六大类,然后再对每一类采用识别算法进行识别,从而提高了分类速度和鲁棒性。 3/)将Gabor特征提取和二维的主成分分析应用到交通标志的识别上,并基于光照控制预处理、Gabor特征提取、二维的主成分分析、分类方法/(包括模板匹配、BP神经网络以及支持向量机/)等理论,提出了四种交通标志牌的识别方法。而后针对不同图像库使用同一方法做了对比实验,发现经过光照控制的图像库的对提高方法识别率有较大帮助,并且在同一图像库条件下,对� Traffic signs from natural scenes are received from the video on board and to be put to computers. Then through the image processing using the developed algorithms in computer, traffic signs can be recognized at real time. But the study of recognition of traffic signs from natural scenes is more difficult and with more challenge than the study in non-natural scenes. This dissertation combines the practical situation in China, Image Library for traffic signs, Image pre-processing algorithm for traffic signs, Image feature extraction of traffic signs, classification of traffic signs and Criterion for traffic signs have been developed in this dissertation. The content and the main results achieved are as follows: /(1/)Because the specific form of traffic signs in different country are not identical. And domestic traffic sign recognition starts late. Therefore, there are not yet publicly available database of traffic signs in china. Image Library for traffic signs in two ways. Firstly, transform the standard traffic sign image by preprocessing transformation /(such as rotation, distortion, adding noise/) to expand the number of traffic signs, improving the robustness of recognition; Secondly, capture the traffic signs through different angles and distance in the real scene, and then make up traffic sign image database by cutting out traffic sign from the real image with detection algorithm in our laboratory. /(2/)For the classification of traffic signs and the traffic signs need to identify, summed up a hierarchical coarse-to-fine classification strategy and apply it to identify the traffic signs, this improve the classification speed and robustness. /(3/)Gabor feature extraction and two-dimensional principal component analysis applied to traffic sign recognition, and base on lighting control preprocessing, Gabor feature extraction, two-dimensional principal component analysis and classification/(include template matching, BP neural network and support vector machines/), propose four kinds of recogn

关 键 词： 交通标志识别 二维主成分分析 小波 模板匹配 神经网络 支持向量机

领　　域： [自动化与计算机技术] [自动化与计算机技术]