Detection of Miscellaneous Bicycle Lane Based on K-Means Algorithm

Cheng Yuan, Xiangpeng Liu, Dan Wang, Yuhua Cheng
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Abstract

The precise detection of bicycle lanes is quite supportive to cyclist tracking in either Advanced Driver Assistance System (ADAS) or intelligent connected vehicles. This paper proposes the bicycle lane detection approach based on the k-means algorithm. Firstly, the pre-processing steps are performed to obtain the promising region of interest, and then the k-means algorithm is used to classify the data. Afterwards, with the help of the classification index of each data point, the image is re-filled with color to segment the lane lines. The implementation of the algorithm employs Intel OpenCV library. Finally, the nonlocal means denoising is applied to remove the noise and obtain the desired lane lines.
基于k均值算法的杂项自行车道检测
无论是在高级驾驶辅助系统(ADAS)还是在智能网联汽车中,对自行车道的精确检测都对骑行者的跟踪提供了很大的支持。本文提出了一种基于k-means算法的自行车道检测方法。首先进行预处理步骤,获得感兴趣的有希望区域,然后使用k-means算法对数据进行分类。然后,借助每个数据点的分类索引,对图像重新填充颜色,分割车道线。算法的实现采用英特尔OpenCV库。最后,采用非局部均值去噪方法去除噪声,得到期望的车道线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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