基于分数阶微分Frangi的夜间车道线检测
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作者单位:

1.长安大学信息工程学院;2.西安航空职业技术学院;3.长安大学

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中图分类号:

TP751

基金项目:

国家自然科学基金(61170147); 国家自然科学基金重点项目(U1401252)


Nighttime lane line detection with fractional differential, Frangi and Hessian
Author:
Affiliation:

1.School of Information Engineering, Chang''an University;2.Xi''an Aeronautical Polytechnic Institute;3.Chang an University

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    摘要:

    基于机器视觉和图像处理的夜间车道线检测一直是该领域的研究难题,即使是近年的深度学习方法,检测精度只能达到50%左右.为此,研究了一种新的算法,根据车道线的特点和车辆的行驶速度,将视频中多幅图像融合到一幅图像中;利用图像的特点,在区域合并中识别出有效的车道线检测区域;将有效区域分割成新的图像后,采用基于Frangi和Hessian矩阵的算法对图像进行平滑和增强;为了提取车道线的特征点,提出了一种新的分数阶微分模板进行车道线特征点检测,该算法根据车道线在图像中可能的位置,从4个方向检测特征点;在检测出候选点后,应用递归Hough直线变换得到候选车道线,为了确定最终的车道线,一条车道线的角度应介于25°~65°之间,而另一条车道线的角度应介于115°~155°之间,否则,通过降低线点数的阈值继续进行Hough直线检测,直到获得两条车道线为止.通过对数百幅夜间车道线图像的测试,并与深度学习方法和传统的图像分割算法进行比较,新算法的检测准确率可达70%.

    Abstract:

    Lane linedetection at night based on machine vision and image processing has always beena research problem in this field, even with the recent deep learning methods, the detection accuracy can only reach to about 50%. To this end, a new algorithm is proposed in this paper. According to the characteristics of the lane line and the speed of the vehicle, multiple images in a video are fused into one detected image; the effective lane line detection area is identified in the region merging by using the characteristics of the image; after the valid detection region is cut into as a new image, the algorithm based on Frag and Hessian matrix is used to smooth and enhance the image; in order to extract the feature points of lane line, a lane line feature point algorithm is proposed based on a new Fractional differential template, then according to the possible position of lane line in the image, the feature points are detected from four directions. After the candidate points are detected, the candidate lane lines are obtained by recursive Hough line transformation. In order to determine the final lane lines,the angle of one lane line should be between 25° and 65°, while the angle of the other lane line should be between 115° and 155°, otherwise, the Hough line transform is continuedby reducing the threshold of the number of lines until two lane lines areobtained. Through testing hundreds of night lane images, the detection accuracy of the new algorithm can reach to 70% compared with deep learning methods and traditional image segmentation algorithms.

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引用本文格式: 陈卫卫,王卫星,闫 迪. 基于分数阶微分Frangi的夜间车道线检测[J]. 四川大学学报: 自然科学版, 2021, 58: 022001.

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  • 收稿日期:2020-08-22
  • 最后修改日期:2020-10-23
  • 录用日期:2020-11-17
  • 在线发布日期: 2021-04-02
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