本文選題：無人駕駛汽車 + 行道線檢測�。� 參考：《南京理工大學》2017年碩士論文

【摘要】：作為無人駕駛系統(tǒng)和輔助駕駛系統(tǒng)的關鍵技術,行道線檢測方法研究備受關注。本文利用傳統(tǒng)的檢測方法提出并實現了一種新的直道檢測和彎道檢測方法,同時從機器學習的角度實現了較為魯棒的行道線區(qū)域粗定位算法,同時也介紹了本文實驗所用平臺。本文工作主要分為以下四個部分:(1)提出并實現了一種基于投影變換的快速行道線直道檢測方法,并研究了三種行道線過濾機制。當行道線磨損時,傳統(tǒng)方法中的邊緣檢測會無能為力,當出現強光、陰影遮擋時,傳統(tǒng)方法中的二值化方法漏檢率也會升高,因此本文提出利用圖像中的投影信息來提取行道線的候選點。針對行道線圖像中的"近大遠小"現象,本文利用積分圖實現了分段投影變換,也相當于實現了行道線的變分辨率檢測。直線提取階段,本文采用了基于角度估計的霍夫變換方法。針對霍夫變換后直線較多的情況,本文采用對比度判斷、消失點估計以及直線聚類的方法對結果進行處理。實驗結果表明,本方法對強光、陰影遮擋以及磨損等情況具有較強的魯棒性,同時能夠在保證精度的同時滿足無人車的實時性要求。(2)提出并實現了一種基于分段式消失點估計的行道線彎道檢測方法。根據彎道的多消失點特性,本方法采用分段式消失點估計的方法進行彎道判別。在彎道檢測過程中,本方法在前文工作的行道線候選點的基礎上采用分段式霍夫變換的方法提取直線,并加以抽樣獲得曲線候選點,然后通過最小二乘法和三次樣條插值法分別對候選點進行曲線擬合。為了對一些擬合后不合理的彎道結果進行拒識,本方法采用通過相機標定的結果對擬合后的曲線進行后驗。實驗結果表明,本方法對于彎道能夠較好的判別并加以檢測,同時發(fā)現最小二乘法較為魯棒,而三次樣條插值法的誤差更小。(3)實現了一種基于機器學習的行道線候選區(qū)域粗定位算法,該方法能夠對以上方法的魯棒性進行進一步加強。由于行道線具有明顯的梯度和邊緣特征,本文采用了HOG和Haar特征分別對行道線圖像進行處理。本文首先對行道線的HOG梯度方向直方圖特征進行了提取,并配合支持向量機SVM進行了訓練分類。同時本文又提取了行道線的Haar特征,并配合級聯分類器Ababoost加以實現。實驗數據表明,Haar特征配合Adaboost級聯分類器具有較高的檢測率。(4)最后本文介紹了本課題項目所處的無人駕駛汽車實驗平臺的硬件系統(tǒng)和軟件系統(tǒng)設計,本文算法在無人車系統(tǒng)中得到了實驗驗證,并取得了很好的應用效果。
[Abstract]:As the key technology of driverless system and auxiliary driving system, the research of lane detection method has attracted much attention. In this paper, a new method of straight track detection and curve detection is proposed and implemented by using the traditional detection method. At the same time, a more robust algorithm of rough location of line region is realized from the point of view of machine learning, and the platform used in this experiment is also introduced. The work of this paper is divided into four parts as follows: (1) this paper proposes and implements a fast line line detection method based on projection transformation, and studies three kinds of line line filtering mechanisms. When the track line is worn, the edge detection in the traditional method will be powerless, and when there is strong light and shadow occlusion, the miss rate of the traditional binary method will also increase. Therefore, the projection information in the image is used to extract the candidate points of the line path. In view of the phenomenon of "near, far and small" in the image of line trace, this paper realizes the piecewise projection transformation by using integral graph, which is equivalent to the variable resolution detection of line trace. In the phase of line extraction, the Hough transform method based on angle estimation is used in this paper. In this paper, contrast judgment, vanishing point estimation and linear clustering are used to deal with the problem of more lines after Hough transform. The experimental results show that the proposed method is robust to strong light, shadow occlusion and wear. At the same time, it can satisfy the real-time requirement of the unmanned vehicle while ensuring the accuracy.) A new detection method based on segmented vanishing point estimation is proposed and implemented. According to the characteristics of multiple vanishing points, the method of segmental vanishing point estimation is used to distinguish the curve. In the process of curve detection, this method uses segmented Hough transform method to extract straight lines and get curve candidate points by sampling. Then the candidate points were fitted by least square method and cubic spline interpolation method. In order to reject some unreasonable curve results after fitting, this method uses the camera calibration results to carry out a posteriori on the fitted curve. The experimental results show that the method can be used to distinguish and detect the curve, and the least square method is more robust. The error of cubic spline interpolation method is smaller than that of cubic spline interpolation method. It implements a rough location algorithm based on machine learning, which can further enhance the robustness of the above methods. Because of the obvious gradient and edge characteristics of the track, the HOG and Haar features are used to process the line image respectively. Firstly, the feature of HOG gradient direction histogram is extracted, and the training classification is carried out with support vector machine (SVM) SVM. At the same time, the Haar features of the line are extracted and implemented with cascaded classifier Ababoost. Experimental data show that Haar feature and Adaboost cascade classifier have high detection rate. Finally, this paper introduces the hardware system and software system design of the experiment platform of driverless vehicle. The algorithm of this paper has been verified by experiment in the unmanned vehicle system, and has obtained the very good application effect.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U463.6;TP391.41

【參考文獻】

相關期刊論文 前2條

1 沈文超;徐建閩;王艷麗;游峰;;1種基于平行直線對模型的車道檢測方法[J];交通信息與安全;2014年03期

2 楊帆;;無人駕駛汽車的發(fā)展現狀和展望[J];上海汽車;2014年03期

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本文編號：1834440