README.md

---
license: apache-2.0
tags:
- infrared-small-target-detection
- remote-sensing
- computer-vision
- frequency-domain
- pytorch
---

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<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>&nbsp;
    <b>Chenglong Yu</b><sup>1</sup>&nbsp;
    <b>Zexuan Li</b><sup>1</sup>&nbsp;
    <b>Haoyu Tang</b><sup>1</sup>&nbsp;
    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
    <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  

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## 📌 Model Information

### 1. Model Name
**HDNet** (Hybrid Domain Network)

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### 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  

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### 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  

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### 4. Training Data Source

Datasets:

- **IRSTD-1K**
- **NUAA-SIRST**
- **NUDT-SIRST**

Download datasets and place them in:

```
./datasets
```

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## 🚀 Environment Setup

- Ubuntu 22.04  
- Python 3.10  
- PyTorch 2.1.0  
- Torchvision 0.16.2+cu121  
- CUDA 12.1  
- GPU: NVIDIA RTX 3090  

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## 🚀 Training

```bash
python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --epochs 800 --mode 'train'
```

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## 🚀 Testing

```bash
python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --mode 'test' --weight-path './weight/irstd.pkl'
```

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## 📊 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 |

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## 📊 Qualitative Results

Visual results:

https://drive.google.com/drive/folders/1RfoxhoHpjfbRMZHBOvISrJSB5lpoz40t?usp=drive_link

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## ⚠️ Notes

- Based on improvements over MSHNet  
- Uses SLS loss  
- Designed for research purposes  

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## 📝 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}
}
```

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## 📬 Contact

For questions or collaboration, please contact the corresponding author.

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