UCrack-DA: A Multi-Scale Unsupervised Domain Adaptation Method for Surface Crack Segmentation
Surface cracks serve as early warning signals for potential geological hazards, and their precise segmentation is crucial for disaster risk assessment. Due to differences in acquisition conditions and the diversity of crack morphology, scale, and surface texture, there is a significant domain shift...
Saved in:
| Main Authors: | Fei Deng, Shaohui Yang, Bin Wang, Xiujun Dong, Siyuan Tian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/12/2101 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection
by: Siyu Wang, et al.
Published: (2025-01-01) -
Multi-Granularity Domain-Adaptive Teacher for Unsupervised Remote Sensing Object Detection
by: Fang Fang, et al.
Published: (2025-05-01) -
Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?
by: Georgios Simantiris, et al.
Published: (2025-06-01) -
Fine-Grained Style Alignment and Class Balance for Unsupervised Domain Adaptation in Remote Sensing Image Segmentation
by: Yousheng Xu, et al.
Published: (2025-01-01) -
An Improved Generative Adversarial Network-Based and U-Shaped Transformer Method for Glass Curtain Crack Deblurring Using UAVs
by: Jiaxi Huang, et al.
Published: (2024-12-01)