Wali Safat B, Abdullah Majid A, Hannan Mahammad A, Hussain Aini, Samad Salina A, Ker Pin J, Mansor Muhamad Bin
Centre for Integrated Systems Engineering and Advanced Technologies, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Institute of Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia.
Sensors (Basel). 2019 May 6;19(9):2093. doi: 10.3390/s19092093.
The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.
自动交通标志检测与识别(TSDR)系统是先进驾驶辅助系统(ADAS)发展中的一项重要研究。基于视觉的TSDR研究在学术界受到了广泛关注,这主要由三个因素驱动,即检测、跟踪和分类。在过去十年中,已经报道了大量用于TSDR的技术。本文对交通标志检测、跟踪和分类进行了全面综述。算法、方法及其在检测、跟踪和分类方面的详细规格在表格中进行了研究和总结,并列出了相应的关键参考文献。对每个部分都进行了比较研究,以评估TSDR数据、性能指标及其可用性。阐述了现有技术当前存在的问题和挑战,并给出了简要建议,同时讨论了未来驾驶辅助系统研究的进展。希望这篇综述能促使人们加大对未来基于视觉的TSDR系统开发的投入。