本文選題：圖像處理 + 車道線提取��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：智能駕駛車輛在社會(huì)進(jìn)步和科技發(fā)展中成為主要話題,為傳統(tǒng)的汽車解決許多棘手的安全問(wèn)題。因此,智能車輛障礙檢測(cè)技術(shù)的研究越來(lái)越受到重視,而可靠地檢測(cè)前方車輛對(duì)于智能駕駛具有重大的研究意義。本文是通過(guò)在電動(dòng)車前方架設(shè)CCD攝像頭來(lái)獲取道路信息,信息處理過(guò)程包括圖像處理、車道線檢測(cè)、感興趣區(qū)域劃分,對(duì)稱性及幾何特征識(shí)別前方車輛。基于CCD攝像頭的前方車輛檢測(cè)便可以時(shí)刻監(jiān)測(cè)道路前方環(huán)境,對(duì)構(gòu)成潛在安全隱患的前方進(jìn)行檢測(cè)。論文主要研究?jī)?nèi)容包括四個(gè)方面:1.單目視頻流的采集及圖像預(yù)處理。介紹了經(jīng)CCD攝像頭采集到的視頻流是由連續(xù)的靜態(tài)幀組成的,并在檢測(cè)前方車輛前需要對(duì)圖像預(yù)處理。圖像預(yù)處理過(guò)程主要包括圖像的灰度化及二值化、圖像濾波、圖像的邊緣檢測(cè),其中選擇基于Canny算子檢測(cè)圖像邊緣,實(shí)驗(yàn)表明二階微分算子在邊緣檢測(cè)中可以有效提取圖像中邊緣信息。2.感興趣區(qū)域的提取。對(duì)前方車輛進(jìn)行檢測(cè)與識(shí)別時(shí),首先需要確定前方車輛的候選區(qū)域,本文針對(duì)改進(jìn)的Hough變換對(duì)車道線進(jìn)行識(shí)別和標(biāo)注,利用區(qū)域分割方法標(biāo)注感興趣區(qū)域,即感興趣區(qū)域含有待檢測(cè)的前方車輛,并通過(guò)劃分結(jié)果驗(yàn)證系統(tǒng)車道線檢測(cè)的結(jié)果。3.車輛檢測(cè)識(shí)別。在前方車輛檢測(cè)的算法中,本文論述了基于對(duì)稱性及提取車輛特征點(diǎn)等算法,重點(diǎn)在于特征點(diǎn)提取中的改進(jìn)的Harris角點(diǎn)檢測(cè),在實(shí)時(shí)性取得突破,在除去邊界點(diǎn)檢測(cè)方面加以創(chuàng)新并作為改進(jìn)算法的核心思想,為下文對(duì)稱性及幾何特征匹配車輛階段奠定基礎(chǔ),確保了車輛檢測(cè)結(jié)果的準(zhǔn)確性。4.基于時(shí)空上下文的視頻流車輛跟蹤。確定靜態(tài)幀的車輛檢測(cè)結(jié)果后,需要對(duì)視頻流中的車輛進(jìn)行跟蹤,以此作為智能駕駛系統(tǒng)中保持安全距離必不可少的部分。本文用時(shí)空上下文算法在視頻流序列中的車輛追蹤,滿足了圍繞視頻序列前后幀的空間上下文特性。前方車輛檢測(cè)技術(shù)可以有效減少交通事故并保障車輛安全駕駛,障礙檢測(cè)不僅應(yīng)用于交通領(lǐng)域,同時(shí)在工業(yè)應(yīng)用、科學(xué)探測(cè)、救災(zāi)搶險(xiǎn)、國(guó)防軍事等領(lǐng)域也有著廣泛的應(yīng)用前景。
[Abstract]:Intelligent driving vehicle has become the main topic in the progress of society and the development of science and technology, solving many thorny safety problems for traditional cars. Therefore, more and more attention has been paid to the research of intelligent vehicle obstacle detection technology. In this paper, CCD camera is set up in front of electric vehicle to obtain road information. The process of information processing includes image processing, lane detection, region of interest division, symmetry and geometric features recognition. The front vehicle detection based on CCD camera can monitor the road front environment at all times and detect the potential safety hidden danger. The main content of this paper includes four aspects: 1. Monocular video stream acquisition and image preprocessing. This paper introduces that the video stream collected by CCD camera is composed of continuous static frames, and the image preprocessing is needed before detecting the vehicle in front. The process of image preprocessing mainly includes grayscale and binarization of image, image filtering, edge detection of image. Among them, Canny operator is chosen to detect image edge. Experiments show that the second order differential operator can effectively extract the edge information from the image in edge detection. Extraction of regions of interest. In order to detect and identify the vehicle in front, we need to determine the candidate area of the vehicle in front. In this paper, the lane line is identified and marked by the improved Hough transform, and the region segmentation method is used to mark the region of interest. In other words, the region of interest contains the vehicle in front of the vehicle to be detected, and the result of lane line detection is verified by the partition result. 3. Vehicle detection and identification. In the forward vehicle detection algorithm, this paper discusses the algorithms based on symmetry and vehicle feature points, the emphasis is on the improved Harris corner detection in feature point extraction, which makes a breakthrough in real-time. As the core idea of the improved algorithm, it can provide the foundation for the following phase of symmetry and geometric feature matching vehicle, and ensure the accuracy of the vehicle detection results. 4. Video stream vehicle tracking based on temporal and spatial context. After determining the vehicle detection results of the static frame, it is necessary to track the vehicle in the video stream as an essential part of the intelligent driving system to keep a safe distance. In this paper, the spatio-temporal context algorithm is used to track the vehicle in the video stream sequence, which satisfies the spatial context characteristics around the frame before and after the video sequence. The forward vehicle detection technology can effectively reduce traffic accidents and ensure the safe driving of vehicles. Obstacle detection is not only applied in the field of transportation, but also in industrial applications, scientific detection, disaster relief and rescue. National defense military and other fields also have a wide range of applications.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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