Depth-Guided Progressive Network for Object Detection-Reference-Cited by-同舟云学术

Depth-Guided Progressive Network for Object Detection

Published:2022-10 Issue:10 Volume:23 Page:19523-19533
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Ma Jia-Wei¹^ORCID,Liang Min²,Chen Song-Lu¹^ORCID,Chen Feng³,Tian Shu²,Qin Jingyan⁴,Yin Xu-Cheng¹^ORCID

Affiliation:

1. USTB-EEasyTech Joint Laboratory of Artificial Intelligence and the Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China

2. Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China

3. EEasy Technology Company Ltd., Zhuhai, China

4. Department of Industrial Design, University of Science and Technology Beijing, Beijing, China

Funder

National Key Research and Development Program of China

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09732282.pdf?arnumber=9732282

Reference49 articles.

1. Depth map prediction from a single image using a multi-scale deep network;eigen;Proc NeurIPS,2014

2. From big to small: Multi-scale local planar guidance for monocular depth estimation;lee;CoRR,2019

3. IoU-aware single-stage object detector for accurate localization

4. Improving Object Localization with Fitness NMS and Bounded IoU Loss

5. Acquisition of localization confidence for accurate object detection;jiang;Proc ECCV,2018

Cited by 5 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. Shuffled Grouping Cross-Channel Attention-Based Bilateral-Filter-Interpolation Deformable ConvNet With Applications to Benthonic Organism Detection;IEEE Transactions on Artificial Intelligence;2024-09

2. Depth-Enhanced Deep Learning Approach For Monocular Camera Based 3D Object Detection;Journal of Intelligent & Robotic Systems;2024-07-09

3. Deep Feature Compression Method for Collaborative Intelligent Applications;2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT);2024-04-26

4. HFENet: Hybrid Feature Enhancement Network for Detecting Texts in Scenes and Traffic Panels;IEEE Transactions on Intelligent Transportation Systems;2023-12

5. A systematic review of object detection from images using deep learning;Multimedia Tools and Applications;2023-06-24