# EFLNet: Enhancing Feature Learning Networks for Infrared Small Target Detection ## Prerequisite Tested on Windows 10 , with Python 3.7, PyTorch 1.13, NVIDIA 3080. The infrared small target public dataset: The [NUAA-SIRST dataset](https://github.com/YimianDai/sirst) The [NUDT-SIRST dataset](https://github.com/YeRen123455/Infrared-Small-Target-Detection) The [IRSTD-1k dataset](https://github.com/RuiZhang97/ISNet/tree/master) The [MDvsFA dataset](https://github.com/wanghuanphd/MDvsFA_cGAN) The [IRST640 dataset](https://github.com/jzchenriver/IRST640) The bounding box annotation version of the current infrared small target public dataset: download from [BaiduYun Drive](https://pan.baidu.com/s/1Gv1gMCdajtR8pR76Y4iQhg) with code IRST or [Google Drive](https://drive.google.com/file/d/1goc6D3647xrcDChOvaCycG2op4nfMZpp/view?usp=sharing). ## Requirements pip install -r requirements.txt pip install -U openmim mim install mmengine mim install "mmcv>=2.0.0" ## Usage __train__: Download the dataset and put it in the data file python train.py --workers 0 --device 0 --batch-size 8 --data data/NUAA-sirst.yaml --img 640 640 --cfg cfg/EFL.yaml --weights '' --name NUAA --hyp data/hyp.scratch.p5.yaml __test__: python test.py --data data/NUAA-sirst.yaml --img 640 --batch 32 --conf 0.001 --iou 0.5 --device 0 --weights NUAA.pt --name NUAA __inference__: python detect.py --weights runs/train/NUAA.pt --conf 0.5 --img-size 640 --source data/NUAA-sirst/images/test ## Results ### Quantitative Results

Method

NUAA-SIRST

NUDT-SIRST

IRSTD-1k

Pre

Rec

F1

Pre

Rec

F1

Pre

Rec

F1

MDvsFA

0.845

0.507

0.597

0.608

0.192

0.262

0.55

0.483

0.475

AGPCNet

0.39

0.81

0.527

0.368

0.684

0.479

0.415

0.47

0.441

ACM

0.765

0.762

0.763

0.732

0.745

0.738

0.679

0.605

0.64

ISNet

0.82

0.847

0.834

0.742

0.834

0.785

0.718

0.741

0.729

ACLNet

0.848

0.78

0.813

0.868

0.772

0.817

0.843

0.656

0.738

DNANet

0.847

0.836

0.841

0.914

0.889

0.901

0.768

0.721

0.744

ours

0.882

0.858

0.870

0.963

0.931

0.947

0.870

0.817

0.843

Download

weight

weight

weight
### Visual Results ![image](https://github.com/yang19950411/infrared-small-target/blob/main/Visual%20Results.png) ## Citation @article{yang2024eflnet, title={EFLNet: Enhancing Feature Learning Network for Infrared Small Target Detection}, author={Yang, Bo and Zhang, Xinyu and Zhang, Jian and Luo, Jun and Zhou, Mingliang and Pi, Yangjun}, journal={IEEE Transactions on Geoscience and Remote Sensing}, volume={62}, pages={1--11}, year={2024}, publisher={IEEE} }