Pretrained Detail Enhancement Framework for Remote Sensing Object Detection
Remote sensing object detection faces significant challenges due to the varying resolutions of images and the diverse shapes of objects. In this paper, we introduce an innovative approach to enhance object detection networks in remote sensing images by addressing these issues through a novel two-sta...
Saved in:
Main Authors: | Mo Zhou, Yue Zhou, Dawei Yang, Kai Song |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10829622/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution
by: Jie Wang, et al.
Published: (2024-12-01) -
SED-YOLO based multi-scale attention for small object detection in remote sensing
by: Xiaotan Wei, et al.
Published: (2025-01-01) -
ViT-ISRGAN: A High-Quality Super-Resolution Reconstruction Method for Multispectral Remote Sensing Images
by: Yifeng Yang, et al.
Published: (2025-01-01) -
Few-Shot Object Detection in Remote Sensing: Mitigating Label Inconsistencies and Navigating Category Variations
by: Tiancheng Si, et al.
Published: (2025-01-01) -
Object-based change detection method for high-resolution remote sensing image combining shadow compensation and multi-scale fusion
by: Chao WANG, et al.
Published: (2018-09-01)