辅助驾驶自动车道检测方法

Maria C. Brad, A. A. Brad, M. Micea
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引用次数: 0

摘要

本文提出了一种基于机器学习的道路车道检测方法。该方法包括对原始图像帧进行预处理,应用霍夫线变换进行车道的初始检测,计算消失点以确定地平线,确定感兴趣区域(ROI)等几个处理步骤。此外,该方法通过裁剪一个三角形透视区域来补偿摄像机面对道路的未知位置。为了纠正由道路变色和裂缝引起的错误,使用白色和黄色像素的颜色蒙版。通过分析线的斜率确定车道的方向,并将车道坐标链接到图像中心。该方法采用U-Net神经网络,基于Python编程语言和OpenCV图像处理库实现。在最后一节中,我们还与基于卷积神经网络的车道检测方法进行了比较,并讨论了结果。
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Method for Autonomous Lane Detection in Assisted Driving
This paper presents a machine learning-based method for detecting lanes on roads. The proposed approach includes several processing steps such as preprocessing of the original image frames, application of the Hough Line Transform for an initial detection of lanes, computation of the vanishing point to determine the horizon line, and region of interest (ROI) determination. Additionally, the method compensates for the unknown position of the camera facing the road by cropping a triangle-shaped perspective area. To correct errors caused by road discoloration and cracks, a color mask for white and yellow pixels is used. The orientation of the lanes is determined by analyzing the slope of the lines, and the lane coordinates are linked to the image center. The proposed method uses the U-Net neural network and the implementation is based on the Python programming language and OpenCV image processing library. In the final section we also present a comparison with a lane detection method based on convolutional neural networks and discuss the results.
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