End-to-End Object Detection by Sparse R-CNN with Hybrid Matching in Complex Traffic Scenes-Reference-Cited by-同舟云学术

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End-to-End Object Detection by Sparse R-CNN with Hybrid Matching in Complex Traffic Scenes

Published:2023 Issue: Volume: Page:1-14
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Han Xue-juan¹,Qu Zhong¹^ORCID,Wang Shi-yan¹,fang Xia Shu-¹,Wang Sheng-ye¹

Affiliation:

1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

National Natural Science Foundation of China

Science and Technology Research Program of Chongqing Municipal Education Commission

Natural Science Foundation of Chongqing Municipality

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/7448921/10310119.pdf?arnumber=10310119

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle;Information Sciences;2025-01

2. Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

3. Human-Guided Deep Reinforcement Learning for Optimal Decision Making of Autonomous Vehicles;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024

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