Improving Small and Cluttered Object Detection by Incorporating Instance Level Denoising Into Single-Shot Alignment Network for Remote Sensing Imagery-Reference-Cited by-同舟云学术

Improving Small and Cluttered Object Detection by Incorporating Instance Level Denoising Into Single-Shot Alignment Network for Remote Sensing Imagery

Published:2022 Issue: Volume:10 Page:51176-51190
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zakaria Yassin¹^ORCID,Mokhtar Sahar A.¹^ORCID,Baraka Hoda²,Hadhoud Mayada²^ORCID

Affiliation:

1. Electronics Research Institute, Giza, Egypt

2. Department of Computer Engineering, Faculty of Engineering, Cairo University, Giza, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09771435.pdf?arnumber=9771435

Reference52 articles.

1. Instance-Aware Semantic Segmentation via Multi-task Network Cascades

2. D2Det: Towards High Quality Object Detection and Instance Segmentation

3. Arbitrary-Oriented Scene Text Detection via Rotation Proposals

4. R2 CNN: Rotational Region CNN for Arbitrarily-Oriented Scene Text Detection

5. CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery

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