| 成像偏振在车道线检测与识别中的应用 |
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| 引用本文： | 王会峰,张佳佳,赵祥模,魏飞婷,汪贵平.成像偏振在车道线检测与识别中的应用[J].西南交通大学学报,2019,54(2):415-420. |
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| 作者姓名： | 王会峰 张佳佳 赵祥模 魏飞婷 汪贵平 |
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| 作者单位： | 长安大学电子与控制工程学院;陕西省道路交通智能检测与装备工程技术研究中心 |
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| 基金项目： | 中国博士后科学基金资助项目（2015M580805）；陕西省博士后科学基金资助项目（2017BSHEDZZ40）；陕西省交通运输厅科技项目（17-33T，17-16K） |
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| 摘 要： | 复杂环境下的车道检测是目前智能车和辅助安全驾驶研究的难点和热点. 针对外部复杂的道路环境，将光学偏振理论引入传统的车道检测技术，提出了一种基于成像偏振的车道线检测方法. 通过对车道线图像基本特征的分析，首先采集3个角度的特殊环境道路偏振图像，获得偏振度图像；然后对偏振度图像作二值化和图像感兴趣区域的划分；再根据车道线边缘的直线特性，进行道路图像的边缘检测从而可以获得车道边缘；最后通过Hough变换原理提出了改进的Hough算法，并得以实现检测出车道标线，计算出汽车行驶偏角. 通过仿真和实验验证表明，该方法能够准确地检测和识别出复杂环境下的车道线，车道线的检测偏角与实际偏角之间的误差小于0.3°.
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| 关 键 词： | 复杂环境 车道线检测 Hough变换 偏振成像 |
| 收稿时间： | 2016-05-11 |
Lane Line Detection and Recognition by Polarisation Imaging |
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| WANG Huifeng,ZHANG Jiajia,ZHAO Xiangmo,WEI Feiting,WANG Guiping.Lane Line Detection and Recognition by Polarisation Imaging[J].Journal of Southwest Jiaotong University,2019,54(2):415-420. |
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| Authors: | WANG Huifeng ZHANG Jiajia ZHAO Xiangmo WEI Feiting WANG Guiping |
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| Abstract: | The detection of lane lines in an adverse environments is a complex and popular topic in both assisted safe driving and intelligent vehicle research. An optical polarization theory was introduced into traditional lane detection technology, aimed at external adverse road environments. In addition, a lane-line detection method was proposed based on imaging polarization by analysing the basic features of the lane line image. Firstly, a three-angle special environment road polarization-image was collected to obtain a polarization degree image. Secondly, the polarization degree images were converted by binarization, divided first into regions of interest, and then according to the straight-line feature of the lane edge before the edge detection of road images were conducted; hence, the actual lane line edge could be obtained. Finally, the improved Hough algorithm proposed through the Hough transform principle could detect the lane marking; and the vehicle driving deflection angle was calculated. The simulation and experimental results demonstrate this method can accurately detect and identify the lane line in an adverse environment with an error of less than 0.3° between the detected and actual declination angle of the lane line. |
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