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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

The NUDT-SIRST dataset

The IRSTD-1k dataset

The MDvsFA dataset

The IRST640 dataset

The bounding box annotation version of the current infrared small target public dataset: download from BaiduYun Drive with code IRST or Google Drive.

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

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}

}

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