CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer.
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| Title: | CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer. |
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| Authors: | NAJAMUDDIN1 najamuddin@graduate.utm.my, SHEIKH, Usman Ullah1, SHA'AMERI, Ahmad Zuri1 |
| Source: | Archives of Acoustics. 2025, Vol. 50 Issue 2, p161-171. 11p. |
| Subjects: | Underwater acoustics, Machine learning, Transformer models, Signal frequency estimation |
| Abstract: | Underwater acoustic target classification has become a key area of research for marine vessel classification, where machine learning (ML) models are leveraged to identify targets automatically. The major challenge is inserting area-specific understanding into ML frameworks to extract features that effectively distinguish between different vessel types. In this study, we propose a model that uses the coherently averaged power spectral estimation (CAPSE) algorithm. Vessel frequency spectra is first computed through the CAPSE analysis, capturing key machinery characteristics. Further, the features are processed via a vision transformer (ViT) network. This method enables the model to learn more complex relationships and patterns within the data, thereby improving the classification performance. This is accomplished by using self-attention mechanisms to capture global dependencies between features, enabling the model to focus on relationships throughout the entire input. The results, evaluated on standard DeepShip and ShipsEar datasets, show that the proposed model achieved a classification accuracy of 97.98% and 99.19% while utilizing just 1.90 million parameters, outperforming other models such as ResNet18 and UATR-Transformer in terms of both accuracy and computational efficiency. This work offers an improvement to the development of efficient marine vessel classification systems for underwater acoustics applications, demonstrating that high performance can be achieved with reduced computational complexity. [ABSTRACT FROM AUTHOR] |
| Copyright of Archives of Acoustics is the property of Polish Academy of Sciences 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187987802 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22NAJAMUDDIN%22">NAJAMUDDIN</searchLink><relatesTo>1</relatesTo><i> najamuddin@graduate.utm.my</i><br /><searchLink fieldCode="AR" term="%22SHEIKH%2C+Usman+Ullah%22">SHEIKH, Usman Ullah</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22SHA'AMERI%2C+Ahmad+Zuri%22">SHA'AMERI, Ahmad Zuri</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Archives+of+Acoustics%22">Archives of Acoustics</searchLink>. 2025, Vol. 50 Issue 2, p161-171. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Underwater+acoustics%22">Underwater acoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+frequency+estimation%22">Signal frequency estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Underwater acoustic target classification has become a key area of research for marine vessel classification, where machine learning (ML) models are leveraged to identify targets automatically. The major challenge is inserting area-specific understanding into ML frameworks to extract features that effectively distinguish between different vessel types. In this study, we propose a model that uses the coherently averaged power spectral estimation (CAPSE) algorithm. Vessel frequency spectra is first computed through the CAPSE analysis, capturing key machinery characteristics. Further, the features are processed via a vision transformer (ViT) network. This method enables the model to learn more complex relationships and patterns within the data, thereby improving the classification performance. This is accomplished by using self-attention mechanisms to capture global dependencies between features, enabling the model to focus on relationships throughout the entire input. The results, evaluated on standard DeepShip and ShipsEar datasets, show that the proposed model achieved a classification accuracy of 97.98% and 99.19% while utilizing just 1.90 million parameters, outperforming other models such as ResNet18 and UATR-Transformer in terms of both accuracy and computational efficiency. This work offers an improvement to the development of efficient marine vessel classification systems for underwater acoustics applications, demonstrating that high performance can be achieved with reduced computational complexity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Archives of Acoustics is the property of Polish Academy of Sciences 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.24425/aoa.2025.153662 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 161 Subjects: – SubjectFull: Underwater acoustics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Signal frequency estimation Type: general Titles: – TitleFull: CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: NAJAMUDDIN – PersonEntity: Name: NameFull: SHEIKH, Usman Ullah – PersonEntity: Name: NameFull: SHA'AMERI, Ahmad Zuri IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01375075 Numbering: – Type: volume Value: 50 – Type: issue Value: 2 Titles: – TitleFull: Archives of Acoustics Type: main |
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