基于传统图像处理算法的车道线检测

Lane Detection Based on Traditional Image Processing Algorithm

在现代交通驾驶领域中,随着自动驾驶技术的迅速发展,车道线检测也变得至关重要。基于此,文章提出了一种基于传统图像处理算法的车道线检测方法,该方法利用了传统图像处理算法中的滤波算法、Canny边缘检测算法和Hough直线检测算法作为基本算法模型,采用只对ROI中进行检测的措施来满足对于前方车道线的准确检测。在检测中,使用了OpenCV开源图像处理库来对进行上述方法进行实现。此方法可极大减少对前方车道线检测的外界干扰,在汽车实验场中利用该方法,可以比较准确地检测出车辆前方的车道线,并且该算法在一般机器上能够实现实时级的车道线检测。但是在实验过程中,也发现当前方的障碍物较多的时候,所采用的算法不能很好地检测出车道线,对外界的抗干扰能力比较差。

With the rapid development of autonomous driving technology,lane line detection has also become critical.Based on this,a lane line detection method based on traditional image processing algorithms is proposed.This method uses the filtering algorithm,Canny edge detection algorithm and Hough line detection algorithm in the traditional image processing algorithm as the basic algorithm model.The detection measures are taken to satisfy the accurate detection of the lane line ahead.In the detection,the OpenCV open source image processing library is used to implement the above method.This method can greatly reduce the external interference to the detection of the front lane line.Using this method in the automobile test field,the lane line in front of the vehicle can be detected more accurately,and the algorithm can achieve real-time lanes on general machines.Line detection.However,during the experiment,it was also found that when there were more obstacles in the front,the algorithm used could not detect the lane line well,and the anti-interference ability to the outside world was relatively poor.