R-CenterNet+: Anchor-Free Detector for Ship Detection in SAR Images
In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes ma...
Main Authors: | Yuhang Jiang, Wanwu Li, Lin Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/17/5693 |
Similar Items
-
Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images
by: Fei Gao, et al.
Published: (2020-08-01) -
DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images
by: Kun Sun, et al.
Published: (2021-07-01) -
Transitive Transfer Learning-Based Anchor Free Rotatable Detector for SAR Target Detection With Few Samples
by: Quanzhi An, et al.
Published: (2021-01-01) -
CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection
by: Yi Yang, et al.
Published: (2021-06-01) -
A Multilayer Fusion Light-Head Detector for SAR Ship Detection
by: Yunchuan Gui, et al.
Published: (2019-03-01)