文献类型：期刊文献

英文题名：Traffic Light Detection for Self-Driving Vehicles Based on Deep Learning

作者：Pan, WeiGuo[1]; Chen, YingHao[2]; Liu, Bo[2]

第一作者：潘卫国

机构：[1] Beijing Key Laboratory of Information Service Engineering, College of Robotics, Beijing Union University, Beijing, China; [2] College of Applied Science and Technology, Beijing Union University, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

年份：2019

起止页码：63-67

外文期刊名：Proceedings - 2019 15th International Conference on Computational Intelligence and Security, CIS 2019

收录：EI(收录号：20201308355536)

语种：英文

外文关键词：Intelligent vehicle highway systems - Accidents - Intelligent systems - Deep learning - Autonomous vehicles

摘要：Research on traffic light detection and semantics is important in the field of intelligent vehicles. Better detection and clearer semantics can help prevent traffic accidents by self-driving vehicles at busy intersections and thus improve driving safety. However, complex traffic scenes increase the difficulty of detection and recognition algorithm. This paper presents a method to detect and recognize traffic lights based on Faster RCNN. Our work includes an annotated collection of traffic scene image data, which fills a gap in public traffic light data sets. An optimal feature extraction network is selected through experimental comparisons. Effectiveness of the method has been verified on a self-driving vehicle platform. ? 2019 IEEE.