基于本体的道路标牌自动审核

基于本体的道路标牌自动审核 Automatic Verification of Road Signs Based on Ontology 王东升 WANG Dongsheng 1 2 first-author 黄智生 HUANG Zhisheng 2 3 钟宁 ZHONG Ning 1 4 徐丹 XU Dan 2 张笑非 ZHANG Xiaofei 1 2 王直 WANG Zhi 2 北京工业大学国际WIC研究院 北京工业大学国际WIC研究院 International WIC Institute,Beijing University of Technology International WIC Institute,Beijing University of Technology 江苏科技大学计算机科学与工程学院/智能交通研究所 江苏科技大学计算机科学与工程学院/智能交通研究所 Institute of Intelligent Transport System/School of Computer Science and Engineering,Jiangsu University of Science of Technology Institute of Intelligent Transport System/School of Computer Science and Engineering,Jiangsu University of Science of Technology 荷兰阿姆斯特丹自由大学计算机系 荷兰阿姆斯特丹自由大学计算机系 Department of Computer Science,Vrije Universiteit Amsterdam Department of Computer Science,Vrije Universiteit Amsterdam 日本前桥工业大学信息工程系 日本前桥工业大学信息工程系 Department of Systems and Information Engineering,Maebashi Institute of Technology Department of Systems and Information Engineering,Maebashi Institute of Technology 随着城市规模的不断扩大,道路标牌的有效管理是智能交通中的一个关键问题.本文在基于本体有机整合海量、多源、异构语义数据的基础上,研究基于国家路标设置规范的路标牌位置和内容有效性自动审核,是对语义技术在智能交通应用中的有益探索.首先,对地缘信息中的道路、兴趣点及路标建立统一模型,采用本体技术把海量数据在语义层面上关联起来.其次,对国家路标设置规范中的规则进行分类,对每一类规则分别构造规则模板及为其构造带参数的SPARQL查询,从而将路标设置指南进行规范形式化表示,在此基础上实现对路标牌的自动检测.最后,在构建的实验数据集上进行了实验,实验结果证明,提出的方法路牌检测准正确率达到85.1%,召回率达到92.1%. With the cities ever growing and evolving much faster than before,effectively managing road signs is a major problem for Intelligent Transport System（ITS）.This paper focuses on checking the positioning and contents of road signs in compliance with related road sign regulations（RSRs）based on integrating scalable heterogeneous data.Firstly,road network,POI（point of interest）and road sign are unifiedly modeled,then multi-sources of semantic data are integrated by using mediate ontology.Secondly,rules in RSRs are categorized and parameterized templates and SPARQL queries are constructed for each category,and then validity checking of road signs are based on these formally encoded RSRs.We finally evaluate the proposed method on a real data set.The results show that we achieve the correct rate of 85.1% and the recall rate of 92.1%.A detailed evaluation analysis is presented. 语义技术 路标 本体 合法性检测 semantic technology road sign ontology validity checking 10.14188/j.1671-8836.2015.04.014 U495;TP391.1 国家自然科学基金重点国际（地区）合作研究项目（61420106005） 2015-04-01 2021-03-31 4 61