本文關(guān)鍵詞：基于圖像處理的車道線檢測(cè)算法研究　出處：《河南工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 車道線識(shí)別 特征提取 改進(jìn)Adaboost算法 改進(jìn)Hough變換

【摘要】：隨著機(jī)動(dòng)車數(shù)量的不斷增加，交通事故的發(fā)生量逐年上升，造成了巨大的財(cái)產(chǎn)損失和人員傷亡。近些年來，國(guó)內(nèi)外學(xué)者們相繼提出了一些具有重大意義的車道線檢測(cè)算法，但是普遍存在兩方面的問題。第一，當(dāng)路面存在較多或較強(qiáng)干擾信息時(shí)，算法魯棒性不夠強(qiáng)。第二，在這種干擾信息存在的條件下，在保證系統(tǒng)魯棒性的同時(shí)實(shí)時(shí)性又不能滿足系統(tǒng)需求。 為了解決以上兩個(gè)問題，本文提出了一種快速、有效的車道線檢測(cè)算法。為了消除圖像在采集階段引入的噪聲干擾和一些冗余信息，本文首先對(duì)采集到的道路圖像進(jìn)行感興趣區(qū)域劃分、灰度化和圖像增強(qiáng)處理。然后，，根據(jù)車道線具有的線性邊緣特征，采用Gabor濾波處理；為了避免在復(fù)雜路況下圖像分割時(shí)閾值難以確定的問題，針對(duì)車道線的特征設(shè)計(jì)了幾種Haar矩形特征模板來提取車道線特征，再結(jié)合Adaboost算法訓(xùn)練分類器，通過訓(xùn)練最終得到判別函數(shù)來對(duì)特征點(diǎn)進(jìn)行分類。為了避免Adaboost算法的缺陷，針對(duì)車道線的分類問題，設(shè)計(jì)了針對(duì)車道線的改進(jìn)Adaboost算法，在計(jì)算過程中采用多種快速處理方法，在不降低魯棒性的條件下保證了系統(tǒng)實(shí)時(shí)性。最后，使用改進(jìn)的可并行運(yùn)算的Hough變換方法提取車道線的參數(shù)信息并模擬出車道線。 經(jīng)過在多種路況下實(shí)驗(yàn)表明，本文采用的特征提取算法能比傳統(tǒng)算法準(zhǔn)確率高10%左右，并且本文的改進(jìn)Hough變換方法比傳統(tǒng)Hough變換節(jié)約90%以上的時(shí)間消耗。在多種路況下均能快速、準(zhǔn)確識(shí)別出圖像中的道路標(biāo)識(shí)線，能夠滿足車道線識(shí)別算法對(duì)實(shí)時(shí)性和魯棒性的要求。
[Abstract]:With the increasing number of vehicles, traffic accidents happen to rise year by year, caused a great loss of property and casualties. In recent years, domestic and foreign scholars have proposed some significant lane detection algorithm, but generally there are two problems. First, when the road exists more or stronger interference information, the robustness is not strong enough. Second, in the presence of interference information, to ensure the system robustness and real-time and can not meet the requirement of the system.
In order to solve the above two problems, this paper presents a fast and effective lane detection algorithm. In order to eliminate the image noise introduced in the acquisition stage and some redundant information, firstly, the road images collected the interested region, grayscale and image enhancement processing. Then, according to the characteristic of linear edge the lane line, using Gabor filter; in order to avoid the complex conditions of image segmentation threshold is difficult to determine, according to the characteristics of the lane design several Haar rectangular feature templates to extract the lane feature, combined with the Adaboost algorithm to train the classifier through training, finally get the discriminant function to classify the feature points in order to avoid. The shortcomings of Adaboost algorithm, aiming at the problem of classification of lane, the improved Adaboost algorithm for lane design, using a variety of in the process of calculation The fast processing method ensures the real-time performance of the system without decreasing the robustness. Finally, the improved parallel operation Hough transform is used to extract the lane information and simulate the lane line.
The experiments show that in a variety of conditions, the algorithm used to extract features than the traditional algorithm has high accuracy rate of about 10%, improved Hough transform method and the consumption than traditional Hough transform to save more than 90% of the time. In a variety of conditions can quickly, accurately identify the image of road marking line, to meet the requirements lane recognition algorithm of real-time and robustness.

【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;U463.6;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 王宏,何克忠,張鈸;智能車輛的自主駕駛與輔助導(dǎo)航[J];機(jī)器人;1997年02期

2 楊殿閣,李克強(qiáng),鄭四發(fā),侯德藻,連小珉;智能交通系統(tǒng)中的汽車技術(shù)[J];汽車工程;2003年03期

3 郭木河,孫健,何克忠;室外智能移動(dòng)機(jī)器人導(dǎo)航中路標(biāo)識(shí)別算法的研究[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);1998年S1期

4 肖剛;林偉;;基于動(dòng)態(tài)感興趣區(qū)域車道線分段擬合算法[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2011年Z1期

本文編號(hào)：1416421