AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network
As the eutrophication of the water body becomes more and more serious, the algae in the water body grow in large quantities and eventually form harmful algal blooms, causing great harm to the marine ecosystem. Therefore, how to quickly and accurately identify algae and make precautions becomes the k...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10620182/ |
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| author | Dan Liu Guihong Yuan Huachao Tan Yanbo Jiang Hai Bi Yuan Cheng |
| author_facet | Dan Liu Guihong Yuan Huachao Tan Yanbo Jiang Hai Bi Yuan Cheng |
| author_sort | Dan Liu |
| collection | DOAJ |
| description | As the eutrophication of the water body becomes more and more serious, the algae in the water body grow in large quantities and eventually form harmful algal blooms, causing great harm to the marine ecosystem. Therefore, how to quickly and accurately identify algae and make precautions becomes the key to solving this problem. Currently, more than tens of thousands of microalgae species are known around the world, but publicly available data are sparse, many of the species are characterized similarly to each other, and it is currently challenging to train an effective classification model with limited data. Existing few-shot learning classification algorithms that utilize meta-learning for metrics can be a good solution to this problem. In this paper, an AlgaeClass_Net algorithm that combines an improved multi-scale feature fusion with a feature enhancement module is proposed for the fine-grained features of microalgae. Furthermore, it utilizes a metric learning approach to classify few-shot microalgae by calculating the distances between the feature vectors of samples in the query set and the feature vectors of samples in the support set. The experimental results showed that the method achieves 78.55% and 91.20% classification accuracies under different tasks of 5-way 1-shot and 5-way 5-shot, respectively, with an improvement of 3.51% and 4.97% on the suboptimal model, respectively. It provides new research ideas for the identification of marine microalgae and the development and utilization of marine renewable energy. |
| format | Article |
| id | doaj-art-75dc11651a434cb7bbd73374a7e7cbda |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-75dc11651a434cb7bbd73374a7e7cbda2025-01-29T00:01:10ZengIEEEIEEE Access2169-35362025-01-0113162231623710.1109/ACCESS.2024.343683810620182AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement NetworkDan Liu0https://orcid.org/0009-0008-1687-3400Guihong Yuan1https://orcid.org/0009-0009-4238-9257Huachao Tan2https://orcid.org/0009-0000-9448-2570Yanbo Jiang3Hai Bi4https://orcid.org/0000-0003-4205-2389Yuan Cheng5https://orcid.org/0000-0001-6198-5673College of Information Engineering, Dalian Ocean University, Dalian, ChinaCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaHangzhou Yunxi Smart Vision Technology Company Ltd., Dalian, ChinaNingbo Institute of Dalian University of Technology, Ningbo, ChinaAs the eutrophication of the water body becomes more and more serious, the algae in the water body grow in large quantities and eventually form harmful algal blooms, causing great harm to the marine ecosystem. Therefore, how to quickly and accurately identify algae and make precautions becomes the key to solving this problem. Currently, more than tens of thousands of microalgae species are known around the world, but publicly available data are sparse, many of the species are characterized similarly to each other, and it is currently challenging to train an effective classification model with limited data. Existing few-shot learning classification algorithms that utilize meta-learning for metrics can be a good solution to this problem. In this paper, an AlgaeClass_Net algorithm that combines an improved multi-scale feature fusion with a feature enhancement module is proposed for the fine-grained features of microalgae. Furthermore, it utilizes a metric learning approach to classify few-shot microalgae by calculating the distances between the feature vectors of samples in the query set and the feature vectors of samples in the support set. The experimental results showed that the method achieves 78.55% and 91.20% classification accuracies under different tasks of 5-way 1-shot and 5-way 5-shot, respectively, with an improvement of 3.51% and 4.97% on the suboptimal model, respectively. It provides new research ideas for the identification of marine microalgae and the development and utilization of marine renewable energy.https://ieeexplore.ieee.org/document/10620182/Marine microalgaefew-shot learningmulti-scale feature fusionfeature enhancementmetric learning |
| spellingShingle | Dan Liu Guihong Yuan Huachao Tan Yanbo Jiang Hai Bi Yuan Cheng AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network IEEE Access Marine microalgae few-shot learning multi-scale feature fusion feature enhancement metric learning |
| title | AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network |
| title_full | AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network |
| title_fullStr | AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network |
| title_full_unstemmed | AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network |
| title_short | AlgaeClass_Net: Optimizing Few-Shot Marine Microalgae Classification With Multi-Scale Feature Enhancement Network |
| title_sort | algaeclass x005f net optimizing few shot marine microalgae classification with multi scale feature enhancement network |
| topic | Marine microalgae few-shot learning multi-scale feature fusion feature enhancement metric learning |
| url | https://ieeexplore.ieee.org/document/10620182/ |
| work_keys_str_mv | AT danliu algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork AT guihongyuan algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork AT huachaotan algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork AT yanbojiang algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork AT haibi algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork AT yuancheng algaeclassx005fnetoptimizingfewshotmarinemicroalgaeclassificationwithmultiscalefeatureenhancementnetwork |