Hough变换和最小二乘拟合的车道线协调检测

Lane Coordination Detection Based on Hough Transformation and Least Square Fitting

针对车道线检测的实时性和鲁棒性要求,借助车道线参数置信区间和Kalman滤波器,采用基于Hough变换的车道线参数全局提取和基于最小二乘拟合的车道线参数局部小窗口提取相协调的方法对车道线进行检测。通过设定车道线感兴趣区域,利用改进快速中值滤波、车道线特征滤波、梯度方向角直方图筛选和车道线连通性分析等图像预处理算法削减图像数据运算量、增强车道线边缘特征。实车实验表明,在Pentium(R)D2.00GHz的CPU上处理速度达每秒25帧,准确率达97.2%。当道路中存在阴影、车辆和道路标记等干扰因素,以及车道线模糊、对比度较低的情况下,该算法仍能快速准确地提取车道线,具有较强的鲁棒性。

To improve the real time performance and robustness,lane coordination detection is executed,which detects the lane parameter by adopting the global extraction based on Hough transformation and the local windows extraction with the help of the lane parameter confidence interval and the Kalman filter.Image preprocessing is conducted including the improved fast median filter,the lane feature filter,the gradient angular histogram selection,and the lane connectivity analysis to cut the computational expense and enhance the lane edge feature after the lane region of interest is defined.Real vehicle experiments show that the proposed method can run at an average speed of 25 frames per second on a Pentium （R） D 2.00 GHz CPU,and the accuracy can reach 97.2%.Meanwhile,it can robustly detect the lane even if there are some interference factors in the road such as shadow,vehicle and landmark etc.,as well as fuzzy lane boundaries and relatively weak contrast.