基于特征提取与边缘点投票的实时车道线识别  

Real-Time Lane Recognition Based on Feature Extraction and Edge Point Voting

作　　者：杨达 魏长河 贾成禹 叶思琴 Yang Da;Wei Changhe;Jia Chengyu;Ye Siqin(Sany Heavy Industry Co.,Ltd.,Changsha 410199;Changsha University of Science&Technology,Changsha 410114)

机构地区：[1]三一重工股份有限公司,长沙410199 [2]长沙理工大学,长沙410114

出　　处：《汽车工程师》2024年第8期29-35,共7页Automotive Engineer

基　　金：中国科协第九届青年人才托举工程项目(2023-2025)。

摘　　要：针对低算力车载计算平台的车道线检测需求,提出了一种低算力依赖的实时车道线识别方法。考虑车辆行驶过程中的光照变化,提出一种自适应光照的颜色分离方法实现车道特征提取;基于经典的边缘检测与霍夫变换算法,定义有效边缘点形式,通过边缘点投票确定车道直线;利用车道直线对边缘点进行筛选与补充,应用随机抽样一致性算法获取车道曲线方程。试验验证结果表明,所提出方法在低算力处理器上的识别精度高于98%,计算速度为38帧/s,并在多种应用场景下具备稳定性与鲁棒性。To satisfy the requirement of low power consumption vehicle computing platform for lane detection,this paper proposes a low computing power dependent real-time lane recognition method.Considering the variation of illumination during vehicle driving,a color separation method based on adaptive illumination to extract lane characteristics is proposed.The effective edge point form is defined and the lane lines are determined by edge point voting based on the classical edge detection and Hough transform algorithm.The lane lines are used to filter and supplement the edge points and the lane curve equation is obtained by using the random sample consensus algorithm.The results show that the proposed method achieves a recognition accuracy of over 98%and computation speed of 38 frames per second on a low power processor.Furthermore,the method has proven to be stable and robust in a variety of scenarios.

关 键 词：智能驾驶 车道线识别 边缘检测 随机抽样一致性 自适应光照 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]