車輛的誕生不僅拉近了地區(qū)與地區(qū)之間的距離,而且加速了人類社會(huì)的發(fā)展�，F(xiàn)今社會(huì),人們的生活以及與車輛緊緊聯(lián)系在一起,車輛已經(jīng)成為人類生活中不可或缺的一部分。人們享受著汽車為我們帶來(lái)的便利,但是不可忽視的是.隨著道路上的車輛越來(lái)越多,也造成了許許多多的交通問(wèn)題,如交通擁堵,交通事故和交通污染等,這些問(wèn)題時(shí)刻威脅著我們的生活。為了解決這些日益增加的交通問(wèn)題,僅僅依靠傳統(tǒng)的交通治理方法并不能達(dá)到很好的效果,人們需要將交通治理與先進(jìn)的高新技術(shù)結(jié)合起來(lái),才能適應(yīng)現(xiàn)代社會(huì)的發(fā)展,因此智能交通系統(tǒng)（ITS）便應(yīng)運(yùn)而生。智能交通系統(tǒng)融合了各種現(xiàn)代化的高新技術(shù),將人類從繁雜的交通管理任務(wù)中解脫出來(lái),實(shí)現(xiàn)了大范圍的智能化、自動(dòng)化的交通管理。智能交通管路系統(tǒng)可以搭載在交通管理部門(mén),道路設(shè)施或者行人車輛上,輔助車輛駕駛,進(jìn)行道路交通的引導(dǎo)與管理,但是由于技術(shù)局限,目前人們依然無(wú)法研究出能夠大規(guī)模應(yīng)用的智能交通系統(tǒng)。在日常的交通管理過(guò)程中,交通標(biāo)志承載了最多的交通信息。駕駛員可以直觀的從這些交通標(biāo)志中獲取前方道路信息與交通信息,交通部門(mén)也可以利用這些交通標(biāo)志對(duì)道路交通進(jìn)行方便、直接的管理。因此,當(dāng)今的智能交通系統(tǒng)... 

【文章來(lái)源】：華中師范大學(xué)湖北省 211工程院校 教育部直屬院校

【文章頁(yè)數(shù)】：79 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
Abstract
1 Introduction
    1.1 Background and research significance
    1.2 Research status at home and abroad
        1.2.1 Status of research on traffic sign detection
        1.2.2 Status of research on traffic sign recognition
    1.3 Difficulties in the recognition of traffic signs in real roads
    1.4 Main work in this paper
    1.5 Paper organization structure
2 Traffic Sign Detection and Recognition Technology
    2.1 China road traffic sign
    2.2 Traffic sign detection technology
        2.2.1 Color-based traffic sign detection
        2.2.2 Shape-based traffic sign detection
    2.3 Traffic sign recognition
        2.3.1 SVM
        2.3.2 Recognition method based on CNN
    2.4 Traffic sign recognition system architecture
    2.5 Summary of this chapter
3 Detection of Traffic Signs in Real Roads
    3.1 Preprocessing of traffic sign image of real road
        3.1.1 Gamma calibration
        3.1.2 RGB contrast enhancement algorithm
        3.1.3 Adaptive gamma calibration algorithm
    3.2 Color segmentation of traffic signs
        3.2.1 Red and blue color segmentation based on RGB color space
        3.2.2 Red, yellow and blue color separation based on HSV color space
        3.2.3 Combined color segmentation
    3.3 Shape-based traffic sign detection
        3.3.1 Morphological processing
        3.3.2 Shape-based traffic sign detection
    3.4 Overall process of traffic sign detection
    3.5 Summary of this chapter
4 Construction of Traffic Sign Recognition System in Real Roads
    4.1 Establishment and expansion of data sets
    4.2 Traffic sign recognition method based on HOG feature and SVM
        4.2.1 HOG feature extraction
        4.2.2 SVM training
    4.3 Based on improved AlexNet traffic sign recognition method
        4.3.1 Classic AlexNet network
        4.3.2 Recognition of traffic signs based on improved AlexNet
    4.4 Construction of traffic sign recognition system
        4.4.1 MATLAB GUI
        4.4.2 Traffic sign recognition system
    4.5 Traffic sign recognition system performance evaluation
        4.5.1 Campus road traffic sign recognition test
        4.5.2 Off-campus road scene traffic sign recognition test
        4.5.3 Traffic sign recognition test for different resolution images
    4.6 Summary of this chapter
5 Summary and Outlook
    5.1 Summary
    5.2 Deficiencies in existence and prospects for future research work
References
Acknowledgements
Appendix A
    Chinese abstract


【參考文獻(xiàn)】：
期刊論文
[1]最優(yōu)RGB線性組合顏色模型目標(biāo)檢測(cè)方法[J]. 溫芝元,曹樂(lè)平.  計(jì)算機(jī)工程與應(yīng)用. 2015(18)
[2]基于網(wǎng)格搜索的PCA-SVM道路交通標(biāo)志識(shí)別[J]. 吳峰,陳后金,姚暢,郝曉莉.  鐵道學(xué)報(bào). 2014(11)



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