Lane Line Detection and Recognition by Polarisation Imaging
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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°.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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Key words:
- adverse environments /
- lane line detection /
- Hough transformation /
- polarization imaging
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图 8 原图与偏振度图像车道识别对比
Figure 8. Lane recognition contrast of original image and polarization degree image
表 1 车道线检测实验数据结果
Table 1. Lane line detection experimental data
序号 车道线检测偏角 与实际偏角偏差 1 3.059 0.159 2 5.148 0.052 3 9.258 0.258 4 11.847 0.247 5 17.560 0.066 6 20.773 0.173 7 24.365 0.035 8 30.294 0.094 
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