中国激光, 2019, 46 (8): 0804002, 网络出版: 2019-08-13   

基于深度学习的道路标线自动提取与分类方法 下载: 2207次

Automatic Extraction and Classification of Road Markings Based on Deep Learning
黄刚 1,2,*刘先林 3
作者单位
1 首都师范大学资源环境与旅游学院, 北京 100048
2 北京四维远见信息技术有限公司, 北京 100070
3 中国测绘科学研究院, 北京 100830
摘要
道路标线提取与分类是智慧城市建设中需要解决的关键技术之一,也是智能驾驶亟待解决的技术难题。提出了一种基于深度学习的道路标线自动提取与分类方法,通过移动窗口法结合相邻扫描线拓扑关系进行地面点云的提取,并生成强度图像,基于深度学习方法实现道路标线的自动提取与分类,并利用KD树聚类分割算法结合矢量化方案实现道路标线的矢量化。基于实验数据对该方法进行验证分析,结果表明,使用该方法进行自动提取与分类的精度和Fscore分别为92.59%和90.15%,证明了该方法的可行性和准确性。该方法为道路标线的自动提取提供了新思路,使道路标线提取工作变得更准确、高效,提升了道路标线获取与分类的智能化程度。
Abstract
Extraction and classification of road markings are two key technologies to be solved in the construction of an intelligent city and urgent technical problems that must be solved for intelligent driving. Therefore, herein, we propose a method of automatic extraction and classification for road markings based on deep learning. First, the ground point clouds are extracted through the moving-window method combined with the topological relations of adjacent scanning lines, and then the intensity images are generated. Automatic road-marking extraction and classification are realized based on the deep learning method. Road-marking vectorization is performed using the KD tree clustering algorithm and the vectorization scheme. The proposed method is analyzed based on the obtained experimental data. Results show that the precision and Fscore of the automatic road-marking extraction and classification reach 92.59% and 90.15%, respectively, proving the feasibility and accuracy of this method. Thus, the proposed method provides a new idea for automatic road-marking extraction and improves its accuracy, efficiency, and intelligent degree of road-marking acquisition and classification.

黄刚, 刘先林. 基于深度学习的道路标线自动提取与分类方法[J]. 中国激光, 2019, 46(8): 0804002. Gang Huang, Xianlin Liu. Automatic Extraction and Classification of Road Markings Based on Deep Learning[J]. Chinese Journal of Lasers, 2019, 46(8): 0804002.

本文已被 13 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!