文献类型：期刊文献

英文题名：Dual model-based traffic light and sign detection using prior information

作者：Pan, Weiguo[1,2]; Pan, Feng[2]; Fu, En[1,2]

第一作者：潘卫国

通讯作者：Pan, Weiguo

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, 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

年份：2020

卷号：16

期号：8

起止页码：1203-1214

外文期刊名：International Journal of Performability Engineering

收录：EI(收录号：20204409418476);Scopus(收录号：2-s2.0-85094177555)

基金：This work was supported by the National Natural Science Foundation of China (No. 61802019, 61871039) and the Beijing Municipal Education Commission Science and Technology Program (No.KM201911417009, KM201911417001).

语种：英文

外文关键词：Digital storage - Object detection - Road vehicles - Roads and streets

摘要：Traffic light and traffic sign detection are important in the field of self-driving. They can guide vehicles to drive safely on the road. It is difficult for existing algorithms of object detection to detect targets simultaneously and achieve high accuracy. In this paper, a dual-model framework is proposed to detect traffic light and signs for a self-driving vehicle based on prior information. This framework can switch the detection model according to the prior information. The color information of the traffic sign is used to extract the ROI and improve the detection efficiency. The work of this paper also includes collecting and annotating a large amount of image data to apply the model trained on the proposed framework to self-driving. The proposed framework is verified on a real road test of a self-driving vehicle. ? 2020 Totem Publisher, Inc.