本文選題：智能移動設備 + 雙目視覺��； 參考：《電子科技大學》2017年碩士論文

【摘要】：近年來針對室外場景的智能移動設備研究愈演愈熱,交通安全問題已成為該研究領域的熱點,而道路檢測技術無疑是其中的一大重點。道路檢測的主要目的是確定當前環(huán)境的可行區(qū)域,提醒移動設備沿著特定的邊緣躲避障礙,最終到達指定位置。目前道路檢測技術發(fā)展的難點在于感知道路中的道路區(qū)域、道路線和障礙物,傳統(tǒng)方法利用的特征主要包括:顏色、紋理、邊緣和路標等信息,然而由于道路環(huán)境的復雜多變,傳統(tǒng)特征已經很難滿足實際項目中的各種需求。針對這一問題,本文提出基于雙目視覺的可行道路檢測方法,在傳統(tǒng)特征的基礎上增加了道路視差信息,以此提高檢測結果的準確度。本文研究內容主要包括:1.研究了基于圖像分割的道路區(qū)域檢測算法。先將圖像進行超像素分割或均勻分塊,然后在分割的基礎上提取特征,利用雙目視覺的優(yōu)點在傳統(tǒng)特征的基礎上增加視差信息,以此提高分類器檢測精度,最后通過相鄰關系矩陣或二位直方圖進行后處理,進一步提高檢測精度。2.研究了基于道路區(qū)域的道路線和障礙物檢測算法。利用道路區(qū)域提取感興趣區(qū)域,提高直線檢測的效率,基于顏色差異、道路邊緣和道路線距離對道路線進行分類,該算法可以有效地檢測出道路邊緣線和斑馬線。利用道路邊緣信息確定障礙物位置,通過視差值得到障礙物的距離信息,基于視頻幀間信息計算出障礙物的相對運動情況。最終將道路線和障礙物的檢測結果融合,確定了圖像中的可行道路。3.搭建嵌入式開發(fā)平臺,實現(xiàn)了移動場景下的實時道路檢測。基于DM8168開發(fā)板搭建雙目視覺嵌入式硬件平臺,將本文算法移植到嵌入式系統(tǒng)中,經過問題分析與優(yōu)化,實現(xiàn)了USB雙目攝像頭輸入,DSP芯片算法處理,顯示器實時展示處理結果的功能。本文通過建立道路數(shù)據(jù)庫進行測試與分析,實驗證明本文算法可以有效地將圖像中的可行道路檢測出來,并且算法執(zhí)行效率高,可以滿足嵌入式運行平臺的實時處理要求。
[Abstract]:In recent years, the research of intelligent mobile devices for outdoor scenes has become hotter and hotter. Traffic safety has become a hot spot in this field, and road detection technology is undoubtedly one of the key points. The main purpose of road detection is to determine the feasible area of the current environment and to remind mobile devices to avoid obstacles along a specific edge and eventually arrive. The difficult point of the development of road detection technology is to know the road areas, road lines and obstacles in the road. The characteristics of the traditional methods mainly include the information of color, texture, edge and road sign. However, because of the complex and changeable road environment, the traditional characteristics have been difficult to meet the needs of the actual projects. This paper proposes a feasible road detection method based on binocular vision, which increases the road parallax information on the basis of traditional features, so as to improve the accuracy of detection results. The main contents of this paper are as follows: 1. the road region detection algorithm based on image segmentation is studied. First, the image is segmented or evenly partitioned. Then the feature is extracted on the basis of the segmentation, and the parallax information is added to the traditional feature based on the advantages of the binocular vision. In order to improve the detection precision of the classifier, the detection precision.2. is further improved by the adjacent relation matrix or two bit histogram after processing, and the road line and obstacle detection based on the road area are examined. The algorithm uses the road area to extract the region of interest and improve the efficiency of the line detection. Based on the color difference, the road edge and route distance are classified. The algorithm can detect the road edge line and the zebra line effectively. The location of the obstacle is determined by the road edge information, and the distance letter is worth to the obstacle by the parallax. Based on the information between video frames, the relative motion of obstacles is calculated. Finally, the detection results of road lines and obstacles are fused, the feasible road.3. in the image is set up to build the embedded development platform, and the real-time road detection in the mobile scene is realized. The embedded hardware platform of the binocular vision based on the DM8168 development board is set up. The algorithm is transplanted into the embedded system. After the problem analysis and optimization, the USB binocular camera input, the DSP chip algorithm processing, the display of the display processing results are displayed in real time. This paper tests and analyzes the road database by establishing the road database. The experiment proves that the algorithm can effectively detect the feasible road in the image. Moreover, the algorithm is efficient and can meet the real-time processing requirements of the embedded platform.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41

【參考文獻】

相關期刊論文 前10條

1 劉家銀;唐振民;吳國星;;基于MVSVM和超像素的可通行區(qū)域檢測方法[J];華中科技大學學報(自然科學版);2015年S1期

2 孫維廣;;一種道路斑馬線的檢測識別方法研究[J];科技創(chuàng)新與應用;2015年10期

3 曹騰;項志宇;劉濟林;;基于視差空間V-截距的障礙物檢測[J];浙江大學學報(工學版);2015年03期

4 楊國青;逄偉;李秀梅;;基于移動終端的道路邊緣檢測方法[J];計算機系統(tǒng)應用;2015年01期

5 林川;宋偉奇;覃金飛;;基于V-視差的障礙物檢測改進方法[J];科學技術與工程;2014年01期

6 許宏科;秦嚴嚴;;一種基于 Hough 變換的車道線檢測新方法[J];徐州工程學院學報(自然科學版);2013年04期

7 王一丁;徐超;;一種基于改良逆投影變換的道路斑馬線識別方法[J];北方工業(yè)大學學報;2013年03期

8 桑瑞娟;王Y，

本文編號：2105958