基于OpenCV和 YOLOv5的车道线检测与识别

为更加快速、准确识别汽车行驶区域并区分车道，实现无人驾驶，提出一种结合视觉OpenCV 算法和改进 YOLOv5算 法的目标检测跟踪模型进行车道线检测的方法。在图像预处理阶段，首先读取视频图像，把每一帧RGB图像转为灰度图，通 过Canny 算子对图像的边缘轮廓进行提取，然后绘制车道线的掩码区域，并与边缘检测结合，采用ROI 技术提取感兴趣区域， 最后进行概率霍夫变换和最小二乘拟合，将得到的直线绘制到原图像中，最终对每一帧处理后的图像进行输出。目标识别模 块采用卷积神经网络(convolutional neural network,CNN)深度学习方法及 YOLOv5算法进行目标识别处理。实验结果表 明，所提检测算法能够实现准确的车道线检测，实时性和准确性比传统算法高很多，且该方法具有良好的鲁棒性。

Lane detection and recognition based on OpenCV and YOLOv5

In order to quickly and accurately identify the driving area of cars and distinguish lanes,achieving autonomous driving.This paper proposes a method for lane detection using an object detection and tracking model that combines visual OpenCV algorithm and improved YOLOv5 algorithm.In the image preprocessing stage,Firstly,read the video image and convert each frame of RGB image into a grayscale image.secondly,uses the Canny operator to extract the edge contour of the image,and then combines the masked area of lane lines with edge detection,Using ROI technology to extract regions of interest.Finally,perform probability Hough transform and least squares fitting to draw the obtained straight line into the original image,and finally output the processed image for each frame.The target recognition module adopts convolutional neural network(CNN)deep learning method and YOLOv5 algorithm for target recognition processing.The experimental results show that the proposed detection algorithm can achieve accurate lane detection,with much higher real-time and accuracy than traditional algorithms,and this method has good robustness.