- MDFA dataset is available at MDvsFa cGan.
- The SIRST Augment dataset: download from Google Drive or BaiduYun Drive with code
ojd4
.
python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 20 --dataset mdfa --learning-rate 0.05
python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 40 --dataset sirstaug --learning-rate 0.1
python train.py --net-name agpcnet_1 --batch-size 8 --save-iter-step 100 --dataset merged --learning-rate 0.05
python inference.py --pkl-path {checkpoint path} --image-path {image path}
python evaluation.py --dataset {dataset name}
--sirstaug-dir {base dir of sirstaug}
--mdfa-dir {base dir of MDFA}
--pkl-path {checkpoint path}
Methods | Data | Precision | Recall | mIoU | Fmeasure | AUC | Download |
---|---|---|---|---|---|---|---|
AGPCNet | MDFA | 0.5939 | 0.7241 | 0.4843 | 0.6525 | 0.8682 | model |
AGPCNet | SIRST Aug | 0.8323 | 0.8542 | 0.7288 | 0.8431 | 0.9344 | model |
AGPCNet | Merged | 0.7453 | 0.8384 | 0.6517 | 0.7891 | 0.9194 | model |
AGPCNet+AugTarget | MDFA | 0.6482 | 0.7141 | 0.5146 | 0.6795 | 0.8699 | model |
AGPCNet+AugTarget | SIRST Aug | 0.8449 | 0.8704 | 0.7505 | 0.8574 | 0.9378 | model |
AGPCNet+AugTarget | Merged | 0.7576 | 0.8658 | 0.6780 | 0.8081 | 0.9395 | model |
Our Target Augmentation algorithm is based on the random strategy of Random-Erasing, thanks for their contributions. This repository is based on baseline model from AGPCNet and modified part of the code.
If any questions, kindly contact with Shengjia Chen via e-mail: csj_uestc@126.com.
-
Zhong, Zhun, et al. "Random erasing data augmentation." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 07. 2020. [code]
-
Zhang, Tianfang, et al. "AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection." arXiv preprint arXiv:2111.03580 (2021). [code]
If you find this repo useful, please cite our paper.
@inproceedings{chen2023augtarget,
title={AugTarget Data Augmentation for Infrared Small Target Detection},
author={Chen, Shengjia and Zhu, Jiewen and Ji, Luping and Pan, Hongjun and Xu, Yuhao},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}