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A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network.

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Title: A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network.
Authors: Ding, Xing1,2 (AUTHOR), Zhang, Xiangrong1 (AUTHOR) 840039436@qq.com, Liang, Chao1,3 (AUTHOR), Liu, Bo1 (AUTHOR), Niu, Lanjie2 (AUTHOR)
Source: Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-21. 21p.
Subjects: Multisensor data fusion, Infrared technology, Automatic target recognition
Abstract: To address the problem of single-mode vulnerability to complex environments, a multimode fusion network with mutual attention is proposed. This network combines the use of laser, infrared and millimeter wave modalities to leverage the advantages of each mode in different environments, increasing the network's resilience to interference. The study begins with the construction of pixel-level fusion networks, feature-weighted fusion networks and the multimode mutual attention fusion network. A comprehensive introduction to the multimode mutual attention fusion network is given, as well as a comparison with the other two networks. The model is then trained and evaluated using data from glide rocket and drone experiments. Finally, an analysis of the anti-outlier interference capability of the multimode fusion network with mutual attention is carried out. The test results show that the multimode mutual attention fusion network containing a feature fusion attention mechanism has the highest detection performance and anti-interference ability. Without interference, the network achieves a remarkable accuracy of 0.98 for multi-target recognition. In addition, with an accuracy of 0.96, it ensures a high level of stability in various interference environments. In addition, the introduction of multi-scale fusion has improved the rocket's speed adaptability by about 75%. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Xing%22">Ding, Xing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiangrong%22">Zhang, Xiangrong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 840039436@qq.com</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Chao%22">Liang, Chao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Bo%22">Liu, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Niu%2C+Lanjie%22">Niu, Lanjie</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. Dec2025, Vol. 39 Issue 1, p1-21. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Infrared+technology%22">Infrared technology</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+target+recognition%22">Automatic target recognition</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address the problem of single-mode vulnerability to complex environments, a multimode fusion network with mutual attention is proposed. This network combines the use of laser, infrared and millimeter wave modalities to leverage the advantages of each mode in different environments, increasing the network's resilience to interference. The study begins with the construction of pixel-level fusion networks, feature-weighted fusion networks and the multimode mutual attention fusion network. A comprehensive introduction to the multimode mutual attention fusion network is given, as well as a comparison with the other two networks. The model is then trained and evaluated using data from glide rocket and drone experiments. Finally, an analysis of the anti-outlier interference capability of the multimode fusion network with mutual attention is carried out. The test results show that the multimode mutual attention fusion network containing a feature fusion attention mechanism has the highest detection performance and anti-interference ability. Without interference, the network achieves a remarkable accuracy of 0.98 for multi-target recognition. In addition, with an accuracy of 0.96, it ensures a high level of stability in various interference environments. In addition, the introduction of multi-scale fusion has improved the rocket's speed adaptability by about 75%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/08839514.2025.2462371
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      – Code: eng
        Text: English
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        PageCount: 21
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        Type: general
      – SubjectFull: Infrared technology
        Type: general
      – SubjectFull: Automatic target recognition
        Type: general
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      – TitleFull: A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network.
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            – D: 01
              M: 12
              Text: Dec2025
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              Y: 2025
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            – TitleFull: Applied Artificial Intelligence
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