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license: apache-2.0
tags:
- infrared-small-target-detection
- remote-sensing
- computer-vision
- frequency-domain
- pytorch
---
<a id="top"></a>
<div align="center">
<h1>🚀 HDNet: A Hybrid Domain Network with Multi-Scale High-Frequency Information Enhancement for Infrared Small Target Detection</h1>
<p>
<b>Mingzhu Xu</b><sup>1</sup>
<b>Chenglong Yu</b><sup>1</sup>
<b>Zexuan Li</b><sup>1</sup>
<b>Haoyu Tang</b><sup>1</sup>
<b>Yupeng Hu</b><sup>1✉</sup>
<b>Liqiang Nie</b><sup>1</sup>
</p>
<p>
<sup>1</sup>Affiliation (Please update if needed)
</p>
</div>
Official implementation of **HDNet**, a Hybrid Domain Network for Infrared Small Target Detection (IRSTD).
🔗 **Journal:** IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2025
🔗 **Task:** Infrared Small Target Detection (IRSTD)
🔗 **Framework:** PyTorch
---
## 📌 Model Information
### 1. Model Name
**HDNet** (Hybrid Domain Network)
---
### 2. Task Type & Applicable Tasks
- **Task Type:** Infrared Small Target Detection / Remote Sensing
- **Core Task:** Small target detection under complex backgrounds
- **Applicable Scenarios:**
- Infrared surveillance
- Remote sensing target detection
- Low-SNR object detection
---
### 3. Project Introduction
Infrared small target detection is challenging due to low signal-to-noise ratio and complex background interference.
**HDNet** proposes a Hybrid Domain Network that integrates spatial-domain and frequency-domain representations:
- **Spatial Domain Branch:** introduces Multi-scale Atrous Contrast (MAC) module to enhance target perception
- **Frequency Domain Branch:** introduces Dynamic High-Pass Filter (DHPF) to suppress low-frequency background
- Combines complementary representations to improve target-background contrast
### Key Contributions:
- A hybrid-domain framework combining spatial and frequency information
- MAC module for multi-scale small target perception
- DHPF module for adaptive low-frequency suppression
- Extensive validation on three benchmark datasets
---
### 4. Training Data Source
Datasets:
- **IRSTD-1K**
- **NUAA-SIRST**
- **NUDT-SIRST**
Download datasets and place them in:
```
./datasets
```
---
## 🚀 Environment Setup
- Ubuntu 22.04
- Python 3.10
- PyTorch 2.1.0
- Torchvision 0.16.2+cu121
- CUDA 12.1
- GPU: NVIDIA RTX 3090
---
## 🚀 Training
```bash
python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --epochs 800 --mode 'train'
```
---
## 🚀 Testing
```bash
python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --mode 'test' --weight-path './weight/irstd.pkl'
```
---
## 📊 Quantitative Results
| Dataset | mIoU | Pd | Fa |
|-----------|------|----|----|
| IRSTD-1K | 70.26 | 94.56 | 4.33 |
| NUAA-SIRST | 79.17 | 100 | 0.53 |
| NUDT-SIRST | 85.17 | 98.52 | 2.78 |
---
## 📊 Qualitative Results
Visual results:
https://drive.google.com/drive/folders/1RfoxhoHpjfbRMZHBOvISrJSB5lpoz40t?usp=drive_link
---
## ⚠️ Notes
- Based on improvements over MSHNet
- Uses SLS loss
- Designed for research purposes
---
## 📝 Citation
```bibtex
@ARTICLE{11017756,
author={Xu, Mingzhu and Yu, Chenglong and Li, Zexuan and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={HDNet: A Hybrid Domain Network With Multiscale High-Frequency Information Enhancement for Infrared Small-Target Detection},
year={2025},
volume={63},
pages={1-15},
doi={10.1109/TGRS.2025.3574962}
}
```
---
## 📬 Contact
For questions or collaboration, please contact the corresponding author.
---
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