交通標(biāo)志論文：自然場景下交通標(biāo)志圖像識(shí)別方法研究

1、交通標(biāo)志論文：自然場景下交通標(biāo)志圖像識(shí)別方法研究【中文摘要】隨著社會(huì)進(jìn)步和經(jīng)濟(jì)的發(fā)展，我國的公路交通行業(yè) 得到了持續(xù)、快速地發(fā)展。高度發(fā)達(dá)的現(xiàn)代交通為人類的生活帶來了 便利,但同時(shí)交通安全、交通擁擠等問題也變得越來越嚴(yán)重。為了解 決這些問題，智能交通系統(tǒng)ITS (Intelligent Traffic System)這一研究領(lǐng)域應(yīng)運(yùn)而生。道路交通標(biāo)志識(shí)別系統(tǒng)TSR (Traffic Sig nsRecognition)作為智能交通系統(tǒng)研究方向，已成為國內(nèi)外學(xué)者研究的 熱點(diǎn)之一，它通過安裝在機(jī)動(dòng)車輛上的攝像機(jī)攝取自然場景圖像，并將圖像送至系統(tǒng)的圖像處理模塊進(jìn)行圖像理解、交通標(biāo)志檢測與識(shí)別最后將識(shí)

2、別結(jié)果告知駕駛員，以達(dá)到增強(qiáng)道路交通安全、降低交通擁 擠的。道路交通標(biāo)志中，警告標(biāo)志、禁令標(biāo)志和指示標(biāo)志是三種最重 要、也是最常見的交通標(biāo)志，它們均具有特定的顏色和形狀用以區(qū)分 其他物體，以達(dá)到提醒駕駛員或行人的。十幾年以來，道路交通標(biāo)志識(shí) 別研究有了很好的進(jìn)展，并取得了一定的研究成果，但因背景復(fù)雜性 以及光照等各種影響因素的存在，導(dǎo)致了它比非自然場景下的目標(biāo)識(shí) 別更具有挑戰(zhàn)性，其影響因素主要表現(xiàn)在以下方面：光照條件時(shí)常變 換且不可控、車輛震動(dòng)導(dǎo)致攝取的圖像模糊、交通標(biāo)志被損壞、被污 染或被遮擋、交通標(biāo)志顏色褪色、雨霧等惡劣天氣的存在，以及投影失真、尺度變換、傾斜、相同顏色背景等。從交通標(biāo)志的

3、顏色信息和 形狀特征出發(fā)，研究一種交通標(biāo)志的智能檢測算法。該算法主要包括 基于HSV(Hue-Saturation-Values) 顏色空間的交通標(biāo)志圖像分割和基于顏色與形狀的交通標(biāo)志檢測兩部分。首先將RGB(Red-Green-Blue) 圖像轉(zhuǎn)換至受光照影響較小的 HSV顏色空間，通過提取不同顏色的閾 值范圍來定位目標(biāo)區(qū)域；再根據(jù)目標(biāo)區(qū)域的幾何形狀來劃分警告、禁令和指示三種不同類別的交通標(biāo)志，完成交通標(biāo)志圖像的檢測。針對(duì) 自然場景下影響交通標(biāo)志檢測效果的不利因素，研究了基于多尺度 Retinex的交通標(biāo)志圖像增強(qiáng)和仿射變換的三角形交通標(biāo)志校正算法，以及規(guī)范化圓形交通標(biāo)志和矩形交通標(biāo)志的算法

4、。實(shí)驗(yàn)結(jié)果表明，該智能檢測算法能克服光照、圖像模糊、雨霧天氣、尺度變換等多種不 利因素帶來的影響，具有較高的魯棒性，驗(yàn)證了算法的有效性。支持向 量機(jī)是一種新的自學(xué)習(xí)算法，它建立在統(tǒng)計(jì)學(xué)習(xí)理論和結(jié)構(gòu)風(fēng)險(xiǎn)最小 原理之上,在小樣本的模式識(shí)別(分類)中具有很好的優(yōu)勢。交通標(biāo)志 的分類和識(shí)別總是在有限個(gè)樣本中進(jìn)行的，本文基于支持向量機(jī)對(duì)已 檢測出的交通標(biāo)志圖像進(jìn)行識(shí)別研究。挖掘能代表不同交通標(biāo)志特征 的數(shù)據(jù)：Hu不變矩和Zernike不變矩，并比較C-SVM和 v-SVM兩種支 持向量機(jī)分別在Linear核函數(shù)、Polynomial核函數(shù)、RBF核函數(shù)和 Sigmoid核函數(shù)下的交通標(biāo)志分類準(zhǔn)確率，并在

5、此基礎(chǔ)上調(diào)整核函數(shù) 的懲罰因子至最優(yōu)。通過對(duì)不同限速的交通標(biāo)志圖像分類和識(shí)別的實(shí) 驗(yàn)表明,特征數(shù)據(jù)經(jīng)歸一化、核函數(shù)經(jīng)尋優(yōu)之后分類和識(shí)別均取得了 良好的效果。【英文摘要】 With the developme nt of society and economy,the road tran sport in dustry in China has bee n developed con ti nu ously and rapidly. Highly developed moder n tran sportatio n provides the convenience to huma n, howe

6、ver, a series of issues, including the traffic safety and trafficcongestion, are becoming increasing serious. In order to solve these problems, the Intelligent Traffic System(ITS)Applicatio n is come into being, and the road Traffic Sig nRecognition system(TSR), as a sub-topic of ITS research field,

7、 has become a focus to domestic and foreig n scholars. The TSR system can be descript as follows:The natural scene images shot with a camera moun ted on a vehicle, are sent to the image process ing module for image- un dersta nding, traffic sig n detecti on and recog niti on, the n in forms drivers

8、of the final results, as a result, the traffic safety can be enhan ced and the traffic jams can be reduced.The warni ng sig ns, the prohibiti on sig ns, and the directi onal sig ns are the most importa nt and com mon types, there are specific colors and shapes to disti nguish them from other objects

9、 and remi nd drivers or pedestrians.More than a decade, the research of roadTraffic Sign Recog niti on has bee n progressed highly and achieved some results, but the complexity of the backgro und, light, and so on, led to its research more challe ngeable tha n the target-recognitionin non-naturalsce

10、nes. The factors are mai nly as follows:light-c on diti on and its uncon trollablefeature, image-blurring because of vehicle vibration,damaged,con tam in ated or blocked, faded, weather of rain and fog.etc, projectio n distorti on, scale tran sformatio n, titled, same or similar color-background.An

11、intelligent algorithm of detecti on for the trafficsig ns based on the color in formatio nand the shape features was proposed. It consists of two parts, the first one is the image-segme ntati on for traffic sig ns with the HSV(Hue-Saturati on-Value) color space, and the other one is the detecti on f

12、or trafficsig ns using the color in formatio nand the shape features. In this algorithm,RGB(Red-Green-Blue)images were converted into HSVcolor space, then the Region of In terests(ROIs) were located by extract ing the thresholds of differe nt types of colors. Based the geometry of ROIs, the n, warni

13、 ng sig ns, prohibiti on sig ns and directi onal sig ns were divided, in con seque nee, traffic sig n detecti on was done. In order to overcome several of unfav orable factors which are gen erally exist in the n atural sce nes, an image enhan ceme nt algorithm based on multi-scale Reti nex and two c

14、orrecti on approaches for the traffic sig ns are proposed. The first correcti on approach is the Affine tra nsformatio n for the trian gular traffic sig ns, and the sec ond one is thetraffic sig ns. Experime nts were con ducted for traffic sig n detection and the results indicated that this intellig

15、ent algorithm could overcome types of un favorable factors and posses of good robust ness, which verified its effective ness.Support Vector Machi ne(SVM) is a new self-learning algorithm, which built on the Statistical Leaning Theory and the Structural Risk Minimization principle, it shows good adva

16、 ntages in small-sample-pattern-recog niti on (classificatio n).Recog niti on and classificati on of traffic sig ns are alwayscon ducted in limited samples, and tak ing this intoconsideration, it presents an approach for traffic sig ns recog niti on based on SVM. Hu-I nv aria nt-Mome nts and Zernike

17、-Invariant-Momentsrepresent different feature data ofdiffere nt traffic sig ns, and it is compared that the accuracy of traffic sig ns classificati on and recog niti on with twoSupport Vector Machi nes:C-SVM and v-SVM in differe nt types of ker nel fun cti ons,Lin ear kern el, Polyno mial kern el, R

18、BFker neland Sigmoid kern el. And for every kernel fun cti on, its pen alty-factor was adjusted to the optimal. The differe nt Limited-Speed traffic sig ns experime nts were con ducted fortraffic sign classificationand recognitionafter feature datano rmalizati on and kernel fun cti on optimizati on,

19、 and the good results achieved.【關(guān)鍵詞】交通標(biāo)志圖像檢測與識(shí)別Retinex圖像增強(qiáng) 支持向量機(jī)【英文關(guān)鍵詞】 Traffic Sig ns Images Detectio n andRecog niti onRet inex Image Enhan ceme ntSupportVector Mach in e(SVM)【目錄】自然場景下交通標(biāo)志圖像識(shí)別方法研究摘要4-6 Abstract 6-7 引言 11-121 緒論 12-241.1 道路交通標(biāo)志識(shí)別研究的背景及意義12-151.1.1研究背景12-141.1.2研究意義14-151.2道路交通標(biāo)志識(shí)別的

20、國內(nèi)外研究現(xiàn)狀15-221.2.2交通標(biāo)志圖像分割與定位算法研究現(xiàn)狀18-201.2.3 交通標(biāo)志分類算法研究現(xiàn)狀20-211.2.4 交通標(biāo)志識(shí)別算法研究現(xiàn)狀21-221.3論文的結(jié)構(gòu)與內(nèi)容安排22-242道路交通標(biāo)志識(shí)別基礎(chǔ)24-362.1引言242.2道路交通標(biāo)志的基礎(chǔ)知識(shí)介紹24-292.2.1警告標(biāo)志25 2.2.2禁令標(biāo)志25-282.2.3 指示標(biāo)志28-292.3典型的彩色空間模型29-332.3.1 RGB彩色空間 292.3.2 HSI彩色空間29-322.3.3 HSV 彩色空間 32-332.3.4 YUV 彩色空間332.4道路交通標(biāo)志識(shí)別系統(tǒng)的框架設(shè)計(jì)33-342.5本章小結(jié)34-363統(tǒng)計(jì)學(xué)習(xí)理論與支持向量機(jī) 36-473.1引言363.2統(tǒng)計(jì)學(xué)習(xí)理論 36-393.2.1 VC 維373.2.2 結(jié)構(gòu)風(fēng)險(xiǎn)最小化37-393.3支持向量機(jī)39-45331最優(yōu)分類超平面