摘要
本文设计了一种基于树莓派嵌入式平台的多道路场景车道线检测算法。在图像预处理阶段,设计了一种车道线的自适应二值化提取算法,通过将待测像素点与其所在菱形空间的顶点进行比较,完整地提取了二值化后的车道线信息;同时与最大类间方差法(OTSU)结合,以图像融合的方式有效滤除了干扰信息。在车道线拟合阶段,对概率霍夫变换进行了斜率约束与限定距离的改进,进行二次滤除干扰信息后准确计算出车道线边缘点。最后使用最小二乘法拟合出车道线。测试结果表明,算法抗干扰能力较强,对多种道路场景的检测准确率可达90.24%,并且在树莓派平台上运行速度为25 fps,满足实时的要求。
This paper designs a lane line detection algorithm for multi-road scenes based on Raspberry Pi embedded platform in the image preprocessing stage.In the image preprocessing stage,an adaptive binarization extraction algorithm for lane lines is designed.By comparing the pixels to be measured with the vertices of the diamond space where they are located,the binarized lane line information is completely extracted.at the same time,combined with the method of maximum classes error(OTSU),the interference information is effectively filtered out by means of image fusion.In the lane line fitting stage,the slope constraint and distance limitation of the progressive probabilistic Hough transform are improved,and the edge points of the lane line are accurately calculated after further filtering out interference information.Finally,the least squares method is used to fit the lane line.The test results show that the algorithm has a stronger anti-interference ability,and the detection accuracy of multiple road scenes can reach 90.24%.And the running speed on the Raspberry Pi platform is 25 fps,which meets the real-time requirements.
作者
宋宝玉
王波涛
Song Baoyu;Wang Botao(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《电子测量技术》
北大核心
2021年第23期93-98,共6页
Electronic Measurement Technology
基金
国家自然科学基金(1L001790201501)项目资助。
关键词
树莓派
图像二值化
干扰滤除
最小二乘法
车道线拟合
Raspberry Pi
binarization
interference filtering
least square method
lane line fitting
