Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background

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Standard

Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background. / Zhang, Tianfang; Li, Lei; Cao, Siying; Pu, Tian; Peng, Zhenming.

I: IEEE Transactions on Aerospace and Electronic Systems, Bind 59, Nr. 4, 2023, s. 4250 - 4261.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Zhang, T, Li, L, Cao, S, Pu, T & Peng, Z 2023, 'Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background', IEEE Transactions on Aerospace and Electronic Systems, bind 59, nr. 4, s. 4250 - 4261. https://doi.org/10.1109/TAES.2023.3238703

APA

Zhang, T., Li, L., Cao, S., Pu, T., & Peng, Z. (2023). Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background. IEEE Transactions on Aerospace and Electronic Systems, 59(4), 4250 - 4261. https://doi.org/10.1109/TAES.2023.3238703

Vancouver

Zhang T, Li L, Cao S, Pu T, Peng Z. Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background. IEEE Transactions on Aerospace and Electronic Systems. 2023;59(4):4250 - 4261. https://doi.org/10.1109/TAES.2023.3238703

Author

Zhang, Tianfang ; Li, Lei ; Cao, Siying ; Pu, Tian ; Peng, Zhenming. / Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background. I: IEEE Transactions on Aerospace and Electronic Systems. 2023 ; Bind 59, Nr. 4. s. 4250 - 4261.

Bibtex

@article{ae901b4ea4ae4a04ac38e9e59af787ce,
title = "Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background",
abstract = "Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.",
keywords = "Context module, Correlation, Feature extraction, Feature fusion, Fuses, Infrared small targets, Neural networks, Object detection, Pyramid context network, Semantics, Task analysis",
author = "Tianfang Zhang and Lei Li and Siying Cao and Tian Pu and Zhenming Peng",
note = "Publisher Copyright: IEEE",
year = "2023",
doi = "10.1109/TAES.2023.3238703",
language = "English",
volume = "59",
pages = "4250 -- 4261",
journal = "IEEE Transactions on Aerospace and Electronic Systems",
issn = "0018-9251",
publisher = "Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS

TY - JOUR

T1 - Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background

AU - Zhang, Tianfang

AU - Li, Lei

AU - Cao, Siying

AU - Pu, Tian

AU - Peng, Zhenming

N1 - Publisher Copyright: IEEE

PY - 2023

Y1 - 2023

N2 - Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.

AB - Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called Attention-Guided Pyramid Context Network (AGPCNet). Specifically, we design Attention-Guided Context Block (AGCB) and perceive pixel correlations within and between patches at specific scales via Local Semantic Association (LSA) and Global Context Attention (GCA) respectively. Then the contextual information from multiple scales is fused by Context Pyramid Module (CPM) to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through Asymmetric Fusion Module (AFM) to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.

KW - Context module

KW - Correlation

KW - Feature extraction

KW - Feature fusion

KW - Fuses

KW - Infrared small targets

KW - Neural networks

KW - Object detection

KW - Pyramid context network

KW - Semantics

KW - Task analysis

U2 - 10.1109/TAES.2023.3238703

DO - 10.1109/TAES.2023.3238703

M3 - Journal article

AN - SCOPUS:85147271141

VL - 59

SP - 4250

EP - 4261

JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

SN - 0018-9251

IS - 4

ER -

ID: 335963752