Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background
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Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background. / Zhang, Tianfang; Li, Lei; Cao, Siying; Pu, Tian; Peng, Zhenming.
In: IEEE Transactions on Aerospace and Electronic Systems, Vol. 59, No. 4, 2023, p. 4250 - 4261.Research output: Contribution to journal › Journal article › Research › peer-review
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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