首页    期刊浏览 2024年12月03日 星期二
登录注册

文章基本信息

  • 标题:Traffic Sign Detection for Intelligent Transportation Systems: A Survey
  • 本地全文:下载
  • 作者:Ayoub Ellahyani ; Ilyas El Jaafari ; Said Charfi
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2021
  • 卷号:229
  • 页码:1006
  • DOI:10.1051/e3sconf/202122901006
  • 出版社:EDP Sciences
  • 摘要:Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.
  • 其他摘要:Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.
国家哲学社会科学文献中心版权所有