@inproceedings{0166840dc10f4ccb969731a938989ac9,
title = "Nanetformer: Nested Attention Network With Auxiliary Transformer Enhancement for Infrared Small Target Detection",
abstract = "Infrared small target detection (ISTD) has been widely concerned in certain fields like astronomy, surveillance, and missile early warning system. ISTD is still a challenging task due to the complex backgrounds and small size of targets, which restrict the performance of the convolutional neural networks (CNN) in ISTD. To this end, a dual branch architecture which combines nested attention network and auxiliary transformer enhancement (NANetFormer) is proposed. The CNN-based branch uses channel-spatial-attention embedded U-Net++ architecture to obtain low-level local details of small targets and suppress background noises. The transformer-based branch applies hierarchical self-attention mechanism as an auxiliary enhancement encoder path. Furthermore, we design a local-global feature fusion module to make feature concentration of two branches. Experimental results show that proposed network achieves competitive results compared with other state-of-the-art methods.",
keywords = "Attention mechanism, Feature fusion, Infrared small target detection, Transformer",
author = "Yunqiao Xi and Junping Zhang and Kun Liu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10282272",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6596--6599",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}