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

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)

Installation and Get Started

Required environments:

• Linux
• Python 3.6+
• PyTorch 1.3+
• CUDA 9.2+
• GCC 5+
• MMCV
• cocoapi-aitod

Installation:

This implementation is based on MMDetection 2.24.1. Assume that your environment has satisfied the above requirements, please follow the following steps for installation.

git clone https://github.com/hoiliu-0801/DNTR.git
cd dntr
pip install -r requirements/build.txt
python setup.py develop

Get Started with single GPU

Training DNTR, for example :

python tools/train.py configs/dntr/aitod_DNTR_mask.py.py

Testing DNTR, for example :

python tools/test.py configs/dntr/aitod_DNTR_mask.py.py

Performance

Table 1. Training Set: AI-TOD trainval set, Validation 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	APm
FRCN	R-50	11.1	26.3	7.6	0.0	7.2	23.3	33.6	22.9
ATSS	R-50	12.8	30.6	8.5	1.9	11.6	19.5	29.2	25.8
ATSS w/ DN-FPN	R-50	17.9	41.0	12.9	3.7	16.4	25.3	35.0	33.2
NWD-RKA	R-50	23.4	53.5	16.8	8.7	23.8	28.5	36.0	6.9
DNTR	R-50	26.2	56.7	20.2	12.8	26.4	31.0	37.0	7.6

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

Citation

@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}, }