本文選題：交通標(biāo)志識(shí)別 + 交通標(biāo)志檢測(cè)��； 參考：《南京理工大學(xué)》2014年碩士論文

【摘要】：交通標(biāo)志識(shí)別是智能交通系統(tǒng)(ITS)的重點(diǎn)研究方向之一,該技術(shù)可以應(yīng)用到無(wú)人駕駛車(chē)輛和駕駛員輔助系統(tǒng),為自動(dòng)或半自動(dòng)駕駛車(chē)輛提供有用的道路信息。經(jīng)過(guò)國(guó)內(nèi)外學(xué)者幾十年的研究,交通標(biāo)志識(shí)別領(lǐng)域的理論和實(shí)踐體系逐漸形成,并取得了很多突破性的進(jìn)展。本文主要針對(duì)交通標(biāo)志識(shí)別中的檢測(cè)、特征提取和分類(lèi)方法進(jìn)行了研究。 在交通標(biāo)志檢測(cè)階段,針對(duì)交通標(biāo)志的顏色和形狀特點(diǎn),本文提出了一種基于顏色分割和局部Hough變換的交通標(biāo)志檢測(cè)方法,首先對(duì)交通標(biāo)志圖像進(jìn)行顏色分割,對(duì)分割得到的二值圖像進(jìn)行輪廓跟蹤提取候選區(qū)域,然后依據(jù)候選區(qū)域的RGB均值對(duì)其進(jìn)行形狀預(yù)分類(lèi),接著運(yùn)用局部Hough對(duì)歸一化的候選區(qū)域進(jìn)行形狀的檢測(cè),最后定位交通標(biāo)志區(qū)域。 在進(jìn)行特征提取時(shí),主要研究了局部核Fisher鑒別分析,分別對(duì)基于子模式的核Fisher鑒別分析(Sp-KFDA)口基于模塊的核Fisher鑒別分析(MKFDA)進(jìn)行了分析。結(jié)合交通標(biāo)志信息分布的特點(diǎn),提出了一種基于自適應(yīng)加權(quán)的模塊核Fisher鑒別分析(Aw-MKFDA)進(jìn)行交通標(biāo)志識(shí)別,通過(guò)在K近鄰分類(lèi)器上的比較實(shí)驗(yàn)表明,本文提出的Aw-MKFDA方法比Sp-KFDA方法和MKFDA方法具有更高的識(shí)別率。 通過(guò)對(duì)分類(lèi)錯(cuò)誤樣本的分析,我們發(fā)現(xiàn)相似類(lèi)是導(dǎo)致分類(lèi)錯(cuò)誤的一個(gè)重要原因。為了解決由于相似性引起的誤分類(lèi)問(wèn)題,本文提出了基于相似類(lèi)劃分的兩階段交通標(biāo)志識(shí)別。該方法將交通標(biāo)志識(shí)別過(guò)程分為兩個(gè)階段：第一階段用稀疏表示進(jìn)行相似類(lèi)的大類(lèi)識(shí)別；第二階段用稀疏表示進(jìn)行相似類(lèi)里的具體類(lèi)別識(shí)別。在稀疏方法用于交通標(biāo)志識(shí)別的過(guò)程中,本文提出采用局部字典代替常用的全局字典,解決了交通標(biāo)志大樣本引起的字典過(guò)大問(wèn)題。實(shí)驗(yàn)結(jié)果表明,本文提出的基于相似類(lèi)劃分的兩階段交通標(biāo)志識(shí)別方法能夠有效的提高交通標(biāo)志的識(shí)別率。 最后,本文采用無(wú)人駕駛平臺(tái)實(shí)時(shí)采集的交通標(biāo)志場(chǎng)景圖像進(jìn)行了綜合實(shí)驗(yàn),對(duì)本文提出的檢測(cè)和識(shí)別方法進(jìn)行了驗(yàn)證,并將稀疏表示和局部KFDA的識(shí)別方法進(jìn)行組合,提出了一種基于投票的組合方法。實(shí)驗(yàn)結(jié)果表明,本文提出的方法獲得了比較理想的結(jié)果,并且具有一定的穩(wěn)定性。
[Abstract]:Traffic sign recognition is one of the key research directions of Intelligent Transportation system (ITS). This technology can be applied to driverless vehicles and driver-assisted systems and provide useful road information for autonomous or semi-autonomous vehicles. After decades of research by domestic and foreign scholars, the theory and practice system of traffic sign recognition has gradually formed, and has made a lot of breakthrough progress. In this paper, the detection, feature extraction and classification of traffic sign recognition are studied. In the phase of traffic sign detection, according to the characteristics of color and shape of traffic sign, this paper presents a method of traffic sign detection based on color segmentation and local Hough transform. Firstly, the color segmentation of traffic sign image is carried out. The binary image is extracted by contour tracking, then the candidate regions are pre-classified according to the RGB mean of candidate regions, and then the normalized candidate regions are detected by local Hough. Finally, locate the traffic sign area. In feature extraction, the local kernel Fisher discriminant analysis is mainly studied, and the kernel Fisher discriminant analysis based on the kernel Fisher discriminant analysis (SP-KFDAA) based on sub-pattern is analyzed respectively. Based on the characteristics of traffic sign information distribution, an adaptive weighted modular kernel Fisher discriminant analysis (Aw-M-MKFDA) is proposed for traffic sign recognition. The Aw-MKFDA method proposed in this paper has a higher recognition rate than the Sp-KFDA method and the MKFDA method. Through the analysis of classification error samples, we find that similar classes are an important cause of classification errors. In order to solve the misclassification problem caused by similarity, this paper proposes a two-stage traffic sign recognition based on similarity classification. The method divides the traffic sign recognition process into two stages: in the first stage, the sparse representation is used to identify the large classes of similar classes; in the second stage, the sparse representation is used to identify the specific classes in the similar classes. In the process of sparse method used in traffic sign recognition, a local dictionary is proposed to replace the common global dictionary, which solves the problem of excessive dictionary size caused by large sample of traffic signs. The experimental results show that the proposed two-stage traffic sign recognition method based on similar class partition can effectively improve the recognition rate of traffic signs. Finally, the scene images of traffic signs collected by driverless platform are synthesized, and the detection and recognition methods proposed in this paper are verified, and the sparse representation is combined with the recognition method of local KFDA. A combination method based on voting is proposed. The experimental results show that the proposed method has better results and has certain stability.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP391.41;U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 王坤明,許忠仁;基于不變矩和神經(jīng)網(wǎng)絡(luò)的交通標(biāo)志識(shí)別方法研究[J];計(jì)算機(jī)應(yīng)用研究;2004年03期

2 甘俊英;張有為;;模式識(shí)別中廣義核函數(shù)Fisher最佳鑒別[J];模式識(shí)別與人工智能;2002年04期

3 張靜;何明一;戴玉超;屈曉剛;;多特征融合的圓形交通標(biāo)志檢測(cè)[J];模式識(shí)別與人工智能;2011年02期

4 朱桂英;張瑞林;;基于Hough變換的圓檢測(cè)方法[J];計(jì)算機(jī)工程與設(shè)計(jì);2008年06期

5 成新民;蔣云良;胡文軍;吳小紅;;基于核的Fisher非線性最佳鑒別分析在人臉識(shí)別中的應(yīng)用[J];中國(guó)圖象圖形學(xué)報(bào);2007年08期

，

本文編號(hào)：1902121