RVIFNet: Radar-Visual Information Fusion Network for All-Weather Vehicle Perception in Roadside Monitoring Scenarios

被引：0

作者：

Li, Kong ^{[1
]}

Cui, Hua ^{[2
]}

Dai, Zhe ^{[3
]}

Song, Huansheng ^{[1
]}

机构：

[1] Chang'an University, School of Information Engineering, Xi'an,710064, China

[2] Chang'an University, School of Future Transportation, Xi'an,710064, China

[3] Chang'an University, School of Transportation Engineering, Xi'an,710064, China

来源：

IEEE Sensors Journal | 2024年 / 24卷 / 23期

关键词：

This work was supported by Postdoctoral Science Foundation of China under Grant 2022M710482; and by National Natural Science Foundation of China under Grant 62072053 and 12171234. (Corresponding authors: Hua Cui; Zhe Dai) Kong Li is with the School of Information Engineering; Changu2019an University; Xiu2019an; China (e-mail: kong li@chd.edu.cn). Hua Cui is with the School of Future Transportation; China (e-mail: huacui@chd.edu.cn). Zhe Dai is with the School of Transportation Engineering; China (e-mail: zhedai@chd.edu.cn). Huansheng Song is with the School of Information Engineering; China (e-mail: hshsong@chd.edu.cn).This work was supported in part by the Postdoctoral Science Foundation of China under Grant 2022M710482; in part by the Natural Science Foundation of Shaanxi Province under Grant 2024JC-YBQN-0369; and in part by the National Natural Science Foundation of China under Grant 52402399 and Grant 12171234. The associate editor coordinating the review of this article and approving it for publication was Prof. Seongwook Lee;

D O I：

10.1109/JSEN.2024.3481492

中图分类号：

学科分类号：

摘要：

引用

页码：40105 / 40122

共 3 条

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[3] Unmanned Surface Vessel Visual Object Detection Under All-Weather Conditions with Optimized Feature Fusion Network in YOLOv4
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