为解决在复杂变光照环境下车道标识线的辨识问题,实现全天候环境的车道偏离预警,提出了一种新的车道标识线辨识算法。将基于OTSU算法的自适应图像分割技术用于在不同光照环境下车道标识线的划分,对图像的全局阈值和局部阈值进行加权处理得到自适应阈值,采用45°与135°梯度方向的Sobel算子提取车道边缘信息,通过改进Hough变换方法完成车道标识线的辨识过程。对不同光照环境下的道路图像进行对比实验,结果表明本文方法比传统Hough变换法的识别准确率平均提升了5.7%,检测单帧图像的平均耗时为57.79 ms。所提算法的抵抗干扰性能良好,能够适应各种光照环境,并且能够满足系统实时性的要求。

To realize the lane marker line identification in complex variable light environment and ensure all-weather lane departure warning, a new lane marker line identification algorithm is proposed. The adaptive image segmentation technology based on OTSU algorithm is used for the lane marker line division for different lighting conditions, and then the adaptive threshold is obtained the weighting process for global and partial thresholds. Sobel operators with gradient directions of 45°and 135° are adopted to extract the lane edge information. Finally, improved Hough transform method is used to finish the lane marker line identification. The road images under different lighting conditions are compared, and the results show that the identification accuracy of the proposed method is improved by 5.7% on average compared with that of the traditional Hough transform method, and the average detection time of a single image is 57.79 ms. The proposed algorithm has good anti-interference performance, can adapt to various lighting conditions, and can meet the real-time requirements of the system.