A DeNoising FPN With Transformer R-CNN for Tiny Object Detection-Reference-Cited by-同舟云学术

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A DeNoising FPN With Transformer R-CNN for Tiny Object Detection

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Hou-I¹,Tseng Yu-Wen²,Chang Kai-Cheng²,Wang Pin-Jyun¹,Shuai Hong-Han¹^ORCID,Cheng Wen-Huang³^ORCID

Affiliation:

1. Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

2. Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

3. Department of Computer Science and Information Engineering, National Taiwan University (NTU), Taipei, Taiwan

Funder

National Science and Technology Council, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10518058.pdf?arnumber=10518058

Reference73 articles.

1. DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

2. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

3. Cascade R-CNN: Delving Into High Quality Object Detection

4. Focal Loss for Dense Object Detection

5. FCOS: Fully Convolutional One-Stage Object Detection

Cited by 2 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. An Effective and Lightweight Full-Scale Target Detection Network for UAV Images Based on Deformable Convolutions and Multi-Scale Contextual Feature Optimization;Remote Sensing;2024-08-11

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