本文選題：單目視覺 + 車道線檢測�。� 參考：《哈爾濱工程大學》2014年碩士論文

【摘要】：隨著社會的不斷進步,汽車正為越來越多的人所使用,而相應的,交通事故也越來越多。為解決這一問題,越來越多國家開始研究智能交通系統(tǒng)。而智能交通的核心基礎就是要檢測和跟蹤道路上的車輛,根據(jù)車輛位置信息來避免交通事故的發(fā)生。本文正是采用基于視覺的方法檢測和跟蹤前方車輛的。車輛檢測通常分為兩個步驟。首先確定車輛可能存在的區(qū)域,這其中包括路旁樹木等留下的虛假車輛陰影。接下來就要剔除虛假的車輛陰影,確定車輛的具體位置。正常情況下最有可能會與本車發(fā)生碰撞的前方車輛在本車道內,所以本文首先檢測車道線,根據(jù)車道線縮小車輛檢測的范圍,提高檢測效率和精度。在提取車道線的基礎上,利用車輛底部在道路上的陰影與路面灰度值的對比度較大,確定車輛可能存在的區(qū)域。再融合圖像熵等紋理特征剔除虛假的車輛陰影,準確檢測出前方車輛。本文的主要工作如下:1.改進了基于OTSU大津閾值法的自適應二值化方法,采用通過統(tǒng)計道路樣本區(qū)域灰度值特性的方式來估算道路區(qū)域灰度值,這樣可以避免傳統(tǒng)OTSU對整幅圖像統(tǒng)計灰度值時計算量大且有非路面區(qū)域干擾的缺點,提高算法實時性和準確性。2.在車道線檢測算法中,運用了形態(tài)學方法和邊緣提取方法后,設計了搜索車道線內側邊緣的掃描算法,并通過對比霍夫變換的算法性能,采用了最小二乘法的擬合車道線方法。為進一步提高算法效率,本文采用了車道線跟蹤算法,在前一幀圖像的車道線位置左右各擴展50像素范圍內搜索,大大降低了車道線檢測算法時間。根據(jù)檢測到的車道線結果,本文計算了每幀圖像車輛的偏航角,當偏航角超過給定閾值時即表明車輛即將偏離本車道,此時可發(fā)出光聲等信號提醒司機采取措施。3.在車輛檢測與跟蹤算法中,本文在基于陰影檢測的算法基礎上,結合圖像熵值和灰度圖像對稱性排除虛假車輛區(qū)域,檢測出車輛在圖像中位置信息,并采用基于卡爾曼濾波的跟蹤方法,在保證檢測精度的同時提高了檢測效率,增強了算法的實時性。4.本文建立了安全車距的防碰撞模型,即相對車速與最大制動距離之間的關系,并且給出了基于視覺的測距模型,根據(jù)圖像中檢測到的車輛坐標即可計算出車距,進而估算出碰撞時間。本文使用C++語言利用視覺處理庫OpenCV1.0編寫了前方車輛檢測系統(tǒng)軟件,并采集了多段道路視頻進行實驗。實驗結果表明本文算法滿足實時性要求,在光照條件良好路段能穩(wěn)定的跟蹤前方車輛目標,對于路況復雜情況也具有一定魯棒性。
[Abstract]:With the development of society, more and more people are using cars, and accordingly, more and more traffic accidents. In order to solve this problem, more and more countries begin to study its. The core of Intelligent Transportation (its) is to detect and track vehicles on the road and to avoid traffic accidents according to the information of vehicle location. In this paper, vision-based methods are used to detect and track forward vehicles. Vehicle testing is usually divided into two steps. First, identify areas where the vehicle may exist, including false vehicle shadows left by roadside trees and so on. The next step is to remove the false shadow of the vehicle and determine the exact location of the vehicle. Under normal circumstances, the front vehicle most likely to collide with the vehicle is in the driveway, so this paper first detects the lane line, narrows the range of vehicle detection according to the lane line, and improves the detection efficiency and accuracy. Based on the extraction of the lane line, the contrast between the shadow at the bottom of the vehicle on the road and the gray value of the road surface is great, and the possible area of the vehicle is determined. Then fusion the image entropy and other texture features to eliminate the false shadow of the vehicle and accurately detect the vehicle ahead. The main work of this paper is as follows: 1. An adaptive binarization method based on Otsu Otsu threshold method is improved to estimate the gray value of road area by statistics of the gray value characteristics of road sample area. In this way, the traditional OTSU can avoid the disadvantages of large computation and non-road area interference when the whole image is calculated by using OTSU, and improve the real-time and accuracy of the algorithm. In the lane line detection algorithm, after using morphological method and edge extraction method, a scanning algorithm is designed to search the inner edge of lane line. By comparing the performance of Hough transform, the least square method is used to fit the lane line. In order to further improve the efficiency of the algorithm, a lane tracking algorithm is adopted in this paper, which searches within the range of 50 pixels about the location of the lane line of the previous frame image, which greatly reduces the time of the lane line detection algorithm. Based on the detected lane line results, this paper calculates the yaw angle of the vehicle in each frame image. When the yaw angle exceeds the given threshold, it indicates that the vehicle is about to deviate from the driveway. At this time, the driver can be warned to take action by means of light and sound signals. In the vehicle detection and tracking algorithm, based on the shadow detection algorithm, combined with the image entropy and gray image symmetry to eliminate the false vehicle area, the vehicle position information in the image is detected. The tracking method based on Kalman filter is used to ensure the detection accuracy and improve the detection efficiency, and enhance the real-time performance of the algorithm. In this paper, the anti-collision model of safe vehicle distance is established, that is, the relation between relative speed and maximum braking distance, and the distance measurement model based on vision is given. The distance can be calculated according to the vehicle coordinates detected in the image. Then the collision time is estimated. In this paper, we use C language and OpenCV1.0 to compile the software of the vehicle detection system in front, and collect the video of many sections of the road to carry on the experiment. The experimental results show that the proposed algorithm can meet the real-time requirements and can track the vehicle targets stably in good lighting conditions. It is also robust to complex road conditions.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U495;TP391.41

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