文献类型：期刊文献

英文题名：Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

作者：Liang, Tianjiao[1,2];Bao, Hong[1,2];Pan, Weiguo[1,2];Pan, Feng[1,2]

第一作者：Liang, Tianjiao

通讯作者：Pan, WG[1];Pan, WG[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R 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

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2022

卷号：2022

外文期刊名：JOURNAL OF ADVANCED TRANSPORTATION

收录：;EI(收录号：20221211813485);Scopus(收录号：2-s2.0-85126528775);WOS:【SCI-EXPANDED(收录号:WOS:000778348700002)】；

基金：,is work was supported in part by the National Natural Science Foundation of China under grant nos. 61802019, 61932012, and 61871039, in part by the Beijing Municipal Education Commission Science and Technology Program under grant nos. KM201911417003, KM201911417009, and KM201911417001, and in part by the Beijing Union University Research and Innovation Projects for Postgraduates under grant nos. YZ2020K001 and YZ2021K001.

语种：英文

外文关键词：Signal detection - Roads and streets - Traffic signs - Data acquisition

摘要：Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.