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A DeNoising FPN with Transformer R-CNN for Tiny Object Detection

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# A DeNoising FPN with Transformer R-CNN for Tiny Object Detection

![method](https://raw.githubusercontent.com/hoiliu-0801/DNTR/main/dnfpn_v2.pdf)

A PyTorch implementation and pretrained models for DNTR (DeNoising Transformer R-CNN). We present DN-FPN, a plug-in that suppresses noise generated during the fusion of FPNs. In addition, we renalvate the standard R-CNN to consist of a transformer structure, namely Trans R-CNN.(base)

## News
[2024/7/1]: **DQ-DETR** has been accepted by ECCV 2024. 🔥🔥🔥

[2024/5/3]: **DNTR** has been accepted by TGRS 2024. 🔥🔥🔥

## Other Research Paper on Tiny Object Detection

| Title | Venue | Links | 
|------|-------------|-------|
| **DNTR** | TGRS 2024  | [Paper](https://arxiv.org/abs/2406.05755) \| [code](https://github.com/hoiliu-0801/DNTR) \| [中文解读](https://blog.csdn.net/qq_40734883/article/details/142579516) | 
| **DQ-DETR**| ECCV 2024 | [Paper](https://arxiv.org/abs/2404.03507)  \| [code](https://github.com/hoiliu-0801/DQ-DETR) \| [中文解读](https://blog.csdn.net/csdn_xmj/article/details/142813757) |

## Installation and Get Started

Our model is based on [mmdet-aitod](https://github.com/Chasel-Tsui/mmdet-aitod) and [MMDetection](https://github.com/open-mmlab/mmdetection).

Please follow the following steps for installation.

```shell script
git clone https://github.com/hoiliu-0801/DNTR.git
cd DNTR/mmdet-dntr
# Installation
sh install.sh
```

Get Started with single GPU

Training DNTR, for example :

```
python tools/train.py configs/aitod-dntr/aitod_DNTR_mask.py
```

Testing DNTR, for example :
```
python tools/test.py configs/aitod-dntr/aitod_DNTR_mask.py
```

## Performance
Table 1. **Training Set:** AI-TOD trainval set, **Testing Set:** AI-TOD test set, 36 epochs, where FRCN, DR denotes Faster R-CNN and DetectoRS, respectively.
|Method | Backbone | mAP | AP50 | AP75 |APvt | APt | APs | APm |
|:---:|:---:|:---:|:---:|:---:|:---:|:---: |:---: |:---: |
FRCN | R-50 | 11.1 | 26.3 | 7.6 | 0.0 | 7.2 | 23.3 | 33.6 | 
ATSS | R-50 | 12.8 | 30.6 | 8.5 | 1.9 | 11.6 | 19.5 | 29.2 | 
ATSS w/ DN-FPN | R-50 | 17.9 | 41.0 | 12.9 | 3.7 | 16.4 | 25.3 | 35.0 |
NWD-RKA | R-50 | 23.4 | 53.5 | 16.8 | 8.7 | 23.8 | 28.5 | 36.0 |
DNTR | R-50 | 26.2 | **56.7** | 20.2 | 12.8 | 26.4 | 31.0 | 37.0 | 
DNTR (New) | R-50 | **27.2** | 56.3 | **21.8** | **15.2** | **27.4** | **31.9** | **38.5** |

Table 2. **Training Set:** Visdrone train set, **Validation Set:** Visdrone val set, 12 epochs,
|Method | Backbone |AP| AP50 | AP75 |
|:---:|:---:|:---:|:---:|:---:|
DNTR | R-50 | 34.4 | 57.9 | 35.3 |
UFPMP w/o DN-FPN| R-50 | 36.6 | 62.4 | 36.7 |
UFPMP w/ DN-FPN | R-50 | **37.8** | **62.7** | **38.6** |

## AI-TOD-v1 and AI-TOD-v2 Datasets (Don’t forget to leave us a ⭐)
* Step 1: Download the datasets from the below link.
```sh
https://drive.google.com/drive/folders/1CowS5BrujefWQxxlmOFfUuLOfUUm8w6U?usp=sharing
```

## Pretrained Weights of AI-TOD-v1 and AI-TOD-v2. 
```sh
https://drive.google.com/drive/folders/1anByR3zA85O_1ygZYYJgjbiI7mqcYRhU
```

## Note
If you want to run other baseline method with DN-FPN, please replace /mmdet/models/detectors/two_stage_ori.py with mmdet/models/detectors/two_stage.py.

For example: 
Faster R-CNN: python tools/train.py configs/aitod-dntr/aitod_faster_r50_dntr_1x.py.

## Citation
```bibtex
@ARTICLE{10518058,
  author={Liu, Hou-I and Tseng, Yu-Wen and Chang, Kai-Cheng and Wang, Pin-Jyun and Shuai, Hong-Han and Cheng, Wen-Huang},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={A DeNoising FPN With Transformer R-CNN for Tiny Object Detection}, 
  year={2024},
  volume={62},
  number={},
  pages={1-15},
}

@InProceedings{huang2024dq,
author={Huang, Yi-Xin and Liu, Hou-I and Shuai, Hong-Han and Cheng, Wen-Huang},
title={DQ-DETR: DETR with Dynamic Query for Tiny Object Detection},
booktitle={European Conference on Computer Vision},
pages={290--305},
year={2025},
organization={Springer}
}

DNTR

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