A research on underwater target recognition neural network for small samples
In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering. However, in the face of objective problems such as the lack of...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
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EDP Sciences
2022-02-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2022/01/jnwpu2022401p40/jnwpu2022401p40.html |
| _version_ | 1797641802623221760 |
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| author | WU Yanchen WANG Yingmin |
| author_facet | WU Yanchen WANG Yingmin |
| author_sort | WU Yanchen |
| collection | DOAJ |
| description | In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering. However, in the face of objective problems such as the lack of underwater target samples, the complex underwater sound environment, and the poor sample signal-to-noise ratio, the deep learning also becomes less sensitive due to its own limitations. In this paper, by constructing a variety of target feature extraction methods and a deep neural network model, we obtain the target recognition rate network prediction value after matched different target feature extraction with neural network model. Through comparing experimental results, a new idea of solving small sample target identification through deep neural network deep design is proposed. |
| first_indexed | 2024-03-11T13:50:58Z |
| format | Article |
| id | doaj.art-2a1de612dc9c4c7f869d07bd1d08dfb1 |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-11T13:50:58Z |
| publishDate | 2022-02-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-2a1de612dc9c4c7f869d07bd1d08dfb12023-11-02T08:57:17ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252022-02-01401404610.1051/jnwpu/20224010040jnwpu2022401p40A research on underwater target recognition neural network for small samplesWU Yanchen0WANG Yingmin1School of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversityIn the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering. However, in the face of objective problems such as the lack of underwater target samples, the complex underwater sound environment, and the poor sample signal-to-noise ratio, the deep learning also becomes less sensitive due to its own limitations. In this paper, by constructing a variety of target feature extraction methods and a deep neural network model, we obtain the target recognition rate network prediction value after matched different target feature extraction with neural network model. Through comparing experimental results, a new idea of solving small sample target identification through deep neural network deep design is proposed.https://www.jnwpu.org/articles/jnwpu/full_html/2022/01/jnwpu2022401p40/jnwpu2022401p40.htmlunderwater target identificationdeep learningdeep neural network design |
| spellingShingle | WU Yanchen WANG Yingmin A research on underwater target recognition neural network for small samples Xibei Gongye Daxue Xuebao underwater target identification deep learning deep neural network design |
| title | A research on underwater target recognition neural network for small samples |
| title_full | A research on underwater target recognition neural network for small samples |
| title_fullStr | A research on underwater target recognition neural network for small samples |
| title_full_unstemmed | A research on underwater target recognition neural network for small samples |
| title_short | A research on underwater target recognition neural network for small samples |
| title_sort | research on underwater target recognition neural network for small samples |
| topic | underwater target identification deep learning deep neural network design |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2022/01/jnwpu2022401p40/jnwpu2022401p40.html |
| work_keys_str_mv | AT wuyanchen aresearchonunderwatertargetrecognitionneuralnetworkforsmallsamples AT wangyingmin aresearchonunderwatertargetrecognitionneuralnetworkforsmallsamples AT wuyanchen researchonunderwatertargetrecognitionneuralnetworkforsmallsamples AT wangyingmin researchonunderwatertargetrecognitionneuralnetworkforsmallsamples |