Official PyTorch implementation of the paper:

Robust Object Detection in Optical Remote Sensing Imagery via Partial-Channel Self-Attention and Intensity-Enhanced Feature Fusion

Optical remote sensing object detection remains challenging due to sparse object distribution, large scale variation, and complex background interference. To address these issues, we propose AIRT-Det, a lightweight CNN–Transformer hybrid detector built upon the RT-DETR framework.

AIRT-Det introduces two complementary modules:

Single-Head Self-Attention-based Internal Feature Interaction

• Performs self-attention on only a subset of informative feature channels.
• Reduces redundant global interactions.
• Improves contextual representation efficiency.
• Maintains low computational overhead.

Intensity Enhance Fusion

• Introduces intensity-regulated nonlinear feature modulation.
• Stabilizes cross-scale feature interaction.
• Enhances discriminative object responses.
• Improves multi-scale feature fusion for small and low-contrast targets.

The proposed framework achieves superior detection performance while preserving real-time inference capability.

The overall architecture consists of:

1. CNN Backbone Feature Extraction
2. SHSAIFI-enhanced Transformer Encoder
3. IFusion-based Multi-scale Feature Fusion
4. RT-DETR Decoder

• End-to-End Object Detection
• Real-Time Inference
• Lightweight Design
• Efficient Global Context Modeling
• Adaptive Multi-scale Feature Fusion
• State-of-the-Art Performance on Remote Sensing Benchmarks

Download:

https://captain-whu.github.io/DOTA/

Directory structure:

datasets/
└── DOTA-v1.5/
    ├── images/
    ├── labels/
    └── split/

Download:

http://www.escience.cn/people/liuzikun/DataSet.html

datasets/
└── HRSC2016/
    ├── images/
    ├── annotations/
    └── labels/

Download:

https://gcheng-nwpu.github.io

datasets/
└── DIOR/
    ├── JPEGImages/
    ├── Annotations/
    └── labels/

python val.py \
    --weights runs/train/best.pt \
    --data dota.yaml

The source code associated with this study is publicly available at:

https://github.com/yourname/AIRT-Det

Archived version (DOI):

https://doi.org/xxxxxxxx

If you find this work useful, please cite:

@article{xu2026airtdet,
  title={Robust Object Detection in Optical Remote Sensing Imagery via Partial-Channel Self-Attention and Intensity-Enhanced Feature Fusion},
  author={Xu, Keke and Zhu, Yongzhen and Liu, Xianglei and Zheng, Wei and Li, Huanxu and Zhao, Jiaqi and Chen, Mengchao},
  journal={Scientific Reports},
  year={2026}
}

This work is built upon the following excellent open-source projects:

• RT-DETR
• Ultralytics
• PyTorch

We sincerely thank the authors for making their code publicly available.