ADSUNet: An Accumulation-Difference-Based Siamese U-Shape Network for inter-frame Infrared Dim and Small Target Detection

Here, we provide the pytorch implementation of the paper: ADSUNet: An Accumulation-Difference-Based Siamese U-Shape Network for inter-frame Infrared Dim and Small Target Detection.

Please see requirements.txt for the requirements.

Test

we provide the test data in "./testdata/Sequence1/new“

Test code:

test_demo.py

Train on your data

Data structure

./IRSTD/inter_frame_data

"""
Data set with pixel-level binary labels；
├─A
├─B
├─label
└─list
"""

A: images of t1 phase;

B:images of t2 phase;

label: label maps;

list: contains train.txt, val.txt and test.txt, each file records the image names (XXX.png) in the dataset.

Main train code

main_ADSUnet.py

with the parameter: --gpu_ids=0 --net_G=SiamUnet_conc_diff_cbam --loss=miou --batch_size=32