SVDDD: SAR Vehicle Target Detection Dataset Augmentation Based on Diffusion Model
In the field of target detection using synthetic aperture radar (SAR) images, deep learning-based supervised learning methods have demonstrated outstanding performance. However, the effectiveness of deep learning methods is largely influenced by the quantity and diversity of samples in the dataset....
Saved in:
Main Authors: | Keao Wang, Zongxu Pan, Zixiao Wen |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/2/286 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Data acquisition system for OLED defect detection and augmentation of system data through diffusion model
by: Byungjoon Kim, et al.
Published: (2025-01-01) -
MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection
by: Jun Liu, et al.
Published: (2025-12-01) -
Siamese Neural Networks in Unmanned Aerial Vehicle Target Tracking Process
by: Athraa Sabeeh Hasan Allak, et al.
Published: (2025-01-01) -
Generating and Improving a Dataset of Masked Faces Using Data Augmentation
by: Waleed Ayad, et al.
Published: (2023-06-01) -
Simplified Target Strength Analysis Procedure of an Underwater Vehicle Considering Target Strength Absorbing Materials
by: Jangwoo Kim, et al.
Published: (2025-01-01)