AFE-Net: Attention-Guided Feature Enhancement Network for Infrared Small Target Detection Article Swipe
Keyan Wang
,
Xueyan Wu
,
Peicheng Zhou
,
Zuntian Chen
,
Rui Zhang
,
Liyun Yang
,
Yunsong Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3354244
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3354244
Infrared small target detection is considerably challenging due to the few pixels in targets, low signal-to-noise ratio, and complex background. In this article, we propose an effective attention-guided feature enhancement network (AFE-Net), which can leverage the local and nonlocal features of targets and background in infrared images. The AFE-Net consists of three key modules, namely encoder and decoder interactive guidance (EDIG) module, cascading false alarm removal (CFAR) module, and random scale input (RSI) module. Specifically, in the EDIG module, we employ a CA mechanism on encoding and decoding layers to select feature channels with higher contribution. Then, we impose a bottom-up pointwise attention block to highlight the features of small infrared targets and suppress possible noise by incorporating the low-level detailed features into the high-level semantic features. The CFAR module extracts affluent global features by cascading nonlocal operations of different layers, which can remove clutters with similar features to infrared targets. The RSI module is placed in front of the entire detection network to extract multiscale features of infrared small targets, which can enhance the robustness of the proposed network. Experimental results on the SIRST dataset and comprehensive comparisons with representative methods demonstrate the superiority of our proposed method.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2024.3354244
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/10400763.pdf
- OA Status
- gold
- Cited By
- 20
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390905672
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- OpenAlex ID
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https://openalex.org/W4390905672Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2024.3354244Digital Object Identifier
- Title
-
AFE-Net: Attention-Guided Feature Enhancement Network for Infrared Small Target DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Keyan Wang, Xueyan Wu, Peicheng Zhou, Zuntian Chen, Rui Zhang, Liyun Yang, Yunsong LiList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2024.3354244Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/4609443/4609444/10400763.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/4609443/4609444/10400763.pdfDirect OA link when available
- Concepts
-
Computer science, Robustness (evolution), Artificial intelligence, Feature (linguistics), Encoder, Decoding methods, False alarm, Pattern recognition (psychology), Feature extraction, Leverage (statistics), Pixel, Computer vision, Algorithm, Chemistry, Philosophy, Biochemistry, Linguistics, Gene, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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