Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model
Abstract Effective classification and identification algorithms of small fishing vessels are the key to strengthen ship management. This paper proposes a classification and recognition method for small fishing boats based on GASF sequence diagram coding, addressing the complex and challenging recogn...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87698-x |
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| _version_ | 1823862513647747072 |
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| author | Jiaqi Deng Xin Liu Gang Du Xu Yang Liangzhong Jiang Chenglong Sun |
| author_facet | Jiaqi Deng Xin Liu Gang Du Xu Yang Liangzhong Jiang Chenglong Sun |
| author_sort | Jiaqi Deng |
| collection | DOAJ |
| description | Abstract Effective classification and identification algorithms of small fishing vessels are the key to strengthen ship management. This paper proposes a classification and recognition method for small fishing boats based on GASF sequence diagram coding, addressing the complex and challenging recognition environment. The method focuses on four typical small fishing vessels, utilizing Gramian Summation Angular Field (GASF) time series images and the Efficiency MPViT (EMPViT) model. Unlike traditional approaches, this study initially employs a high-precision laser sensor to gather one-dimensional contour data of fishing boats. Subsequently, the polynomial fitting method is used to delineate the shape of the fishing boat contour, which is then encoded into a two-dimensional time series image using the GASF encoding method. The enhanced EMPViT model is then applied to classify and identify small fishing vessels, with the results verified through ablation experiments. These experiments demonstrate that the EMPViT model surpasses traditional neural network models such as CNN and ViT in both accuracy and performance, achieving a peak accuracy of 99.98%. |
| format | Article |
| id | doaj-art-cc77abee27204709a15c51934f458084 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cc77abee27204709a15c51934f4580842025-02-09T12:32:21ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-87698-xSmall fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT modelJiaqi Deng0Xin Liu1Gang Du2Xu Yang3Liangzhong Jiang4Chenglong Sun5Southwest Institute of Electronic TechnologySouthwest Institute of Electronic TechnologySouthwest Institute of Electronic TechnologySouthwest Institute of Electronic TechnologySouthwest Institute of Electronic TechnologySouthwest Institute of Electronic TechnologyAbstract Effective classification and identification algorithms of small fishing vessels are the key to strengthen ship management. This paper proposes a classification and recognition method for small fishing boats based on GASF sequence diagram coding, addressing the complex and challenging recognition environment. The method focuses on four typical small fishing vessels, utilizing Gramian Summation Angular Field (GASF) time series images and the Efficiency MPViT (EMPViT) model. Unlike traditional approaches, this study initially employs a high-precision laser sensor to gather one-dimensional contour data of fishing boats. Subsequently, the polynomial fitting method is used to delineate the shape of the fishing boat contour, which is then encoded into a two-dimensional time series image using the GASF encoding method. The enhanced EMPViT model is then applied to classify and identify small fishing vessels, with the results verified through ablation experiments. These experiments demonstrate that the EMPViT model surpasses traditional neural network models such as CNN and ViT in both accuracy and performance, achieving a peak accuracy of 99.98%.https://doi.org/10.1038/s41598-025-87698-xGASF time series imagesOne-dimensional time series codingPolynomial fittingClassification and recognition of small fishing boatsEMPViT deep learning model |
| spellingShingle | Jiaqi Deng Xin Liu Gang Du Xu Yang Liangzhong Jiang Chenglong Sun Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model Scientific Reports GASF time series images One-dimensional time series coding Polynomial fitting Classification and recognition of small fishing boats EMPViT deep learning model |
| title | Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model |
| title_full | Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model |
| title_fullStr | Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model |
| title_full_unstemmed | Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model |
| title_short | Small fishing boat classification and recognition method based on GASF temporal graph coding and EMPViT model |
| title_sort | small fishing boat classification and recognition method based on gasf temporal graph coding and empvit model |
| topic | GASF time series images One-dimensional time series coding Polynomial fitting Classification and recognition of small fishing boats EMPViT deep learning model |
| url | https://doi.org/10.1038/s41598-025-87698-x |
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