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iLearn-Lab/
TGRS25-HDNet

infrared-small-target-detection

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🚀 HDNet: A Hybrid Domain Network with Multi-Scale High-Frequency Information Enhancement for Infrared Small Target Detection

Mingzhu Xu¹ Chenglong Yu¹ Zexuan Li¹ Haoyu Tang¹ Yupeng Hu^1✉ Liqiang Nie¹

¹Affiliation (Please update if needed)

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

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

🚀 Testing

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.

iLearn-Lab/TGRS25-HDNet