PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regressio...
Saved in:
Main Authors: | Weicong Chen, Chenghong Zhang, Yuan Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-07-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/14/7629 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Infrared Small Target Detection Based on Approximate Background Regularization and Bimodal Slice Based Graph Constraints
by: Xiaoling Ge, et al.
Published: (2025-01-01) -
YOLO-SSFA: A Lightweight Real-Time Infrared Detection Method for Small Targets
by: Yuchi Wang, et al.
Published: (2025-07-01) -
PCLC-Net: Parallel Connected Lateral Chain Networks for Infrared Small Target Detection
by: Jielei Xu, et al.
Published: (2025-06-01) -
Lightweight Infrared Small Target Detection Method Based on Linear Transformer
by: Bingshu Wang, et al.
Published: (2025-06-01) -
An Infrared Small Target Detection Algorithm Utilizing Joint Local Contrast Measure
by: Qing Zhao, et al.
Published: (2025-01-01)