本文選題：機(jī)器視覺 + 圖像預(yù)處理　； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：自上世紀(jì)八十年代以來,基于機(jī)器視覺的自主導(dǎo)航已成為了智能車輛駕駛技術(shù)研究領(lǐng)域的主要方向。車道線識別技術(shù)是自主導(dǎo)航的關(guān)鍵技術(shù)之一,國內(nèi)外專家學(xué)者在這一技術(shù)研究領(lǐng)域做了很多研究,目的是提高識別的魯棒性和實時性。本文針對較為復(fù)雜的道路情況下車道線識別率低、擬合不準(zhǔn)確的問題,在保證整個系統(tǒng)實時性的前提下,按照感興趣區(qū)域劃分、圖像預(yù)處理、邊緣檢測、識別與跟蹤的脈絡(luò)進(jìn)行如下研究,以提高識別的魯棒性。文中首先介紹課題研究的背景和意義,并對國內(nèi)外的研究進(jìn)行了分析,明確它們存在的問題。其次,對采集的道路圖像進(jìn)行灰度化,再利用垂直灰度均值分布進(jìn)行初始感興趣區(qū)域分割。預(yù)處理階段分別介紹了圖像平滑、增強(qiáng)和邊緣檢測二值化處理的方法,深入研究了強(qiáng)弱光照下的路面灰度圖像處理,并對各種邊緣檢測算子進(jìn)行了實驗比較,接著設(shè)計改進(jìn)Otsu算法提高了車道線識別度。車道線邊緣檢測階段是對車道線的進(jìn)一步提取,針對噪聲對車道線邊緣識別的干擾問題,重點(diǎn)提出了分區(qū)域識別邊緣角度并加以排除的方法去除異常邊緣線,并對去噪后的邊緣線進(jìn)行了補(bǔ)償。車道線識別和跟蹤階段,分析了傳統(tǒng)的直線檢測和彎道檢測方法,并著重對概率Hough變換及RANSAC算法做了改進(jìn)研究,針對模型靈活性的要求,提出了直線-拋物線型的車道線模型,并設(shè)計了模型區(qū)域分配的方法以解決曲線道路出現(xiàn)位置不定的情況,再利用最小二乘法求出車道線模型的參數(shù)。實驗表明,這種方法面對模型不定的結(jié)構(gòu)化道路具有較好的魯棒性。最后,對得到的初始車道線圖像根據(jù)其直線模型的斜率與截距,利用Kalman濾波來預(yù)測出下一幀的車道線范圍,有效的避免過多的噪聲干擾。通過對本文算法的仿真實驗,證明了本方法具有較好的魯棒性。
[Abstract]:Since 1980's, autonomous navigation based on machine vision has become the main research field of intelligent vehicle driving technology. Lane recognition technology is one of the key technologies of autonomous navigation. Experts and scholars at home and abroad have done a lot of research in this field in order to improve the robustness and real-time of recognition. In order to solve the problem of low recognition rate of lane line and inaccurate fitting under more complicated road conditions, this paper, on the premise of ensuring the real-time performance of the whole system, divides the area of interest according to the region of interest, image preprocessing, edge detection, etc. The sequence of recognition and tracking is studied as follows to improve the robustness of recognition. This paper first introduces the background and significance of the research, analyzes the domestic and foreign research, and clarifies their problems. Secondly, the road image is grayscale, and then the initial region of interest is segmented by using the vertical gray mean distribution. In the preprocessing stage, the methods of image smoothing, enhancement and edge detection binarization are introduced, and the grayscale image processing of road surface under strong and weak illumination is deeply studied, and various edge detection operators are compared experimentally. Then the improved Otsu algorithm is designed to improve the lane line recognition. The phase of lane edge detection is to further extract lane line. Aiming at the interference of noise to lane line edge recognition, this paper puts forward a method to remove abnormal edge line by recognizing edge angle in different regions and removing abnormal edge line. The edge line after denoising is compensated. In the phase of lane line identification and tracking, the traditional methods of line detection and curve detection are analyzed, and the probabilistic Hough transform and RANSAC algorithm are emphatically studied. According to the requirements of flexibility of the model, a straight-parabola lane line model is proposed. The method of model area allocation is designed to solve the problem that the position of the curve road is uncertain, and the parameters of the driveway model are obtained by using the least square method. The experimental results show that this method is robust to structured roads with uncertain models. Finally, according to the slope and intercept of the linear model, the Kalman filter is used to predict the lane range of the next frame. The simulation results show that the proposed method is robust.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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