Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture
Real-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced...
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MDPI AG
2024-10-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/10/1823 |
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| author | Jintao Liu Alfredo Tolón-Becerra José Fernando Bienvenido-Barcena Xinting Yang Kaijie Zhu Chao Zhou |
| author_facet | Jintao Liu Alfredo Tolón-Becerra José Fernando Bienvenido-Barcena Xinting Yang Kaijie Zhu Chao Zhou |
| author_sort | Jintao Liu |
| collection | DOAJ |
| description | Real-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced with the Swin transformer to extract image features more efficiently. Additionally, a squeeze-and-excitation (SE) module is introduced to enhance feature representation by dynamically adjusting the importance of each channel through “squeeze” and “excitation”, making the extracted features more focused and effective. Finally, a multi-scale fusion (MSF) module is added after the back-end to fully utilize the multi-scale feature information, enhancing the model’s ability to capture multi-scale details. The experiment demonstrates that Swin-CSRNet achieved excellent results with MAE, RMSE, and MAPE and a correlation coefficient R<sup>2</sup> of 11.22, 15.32, 5.18%, and 0.954, respectively. Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. Therefore, the proposed method not only counts the number of fish with higher speed and accuracy but also contributes to advancing the automation of aquaculture. |
| format | Article |
| id | doaj-art-adfd57659204446b8ea71ad61efb2ef2 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-adfd57659204446b8ea71ad61efb2ef22025-08-20T02:10:54ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-10-011210182310.3390/jmse12101823Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in AquacultureJintao Liu0Alfredo Tolón-Becerra1José Fernando Bienvenido-Barcena2Xinting Yang3Kaijie Zhu4Chao Zhou5School of Engineering, University of Almeria, 04120 Almeria, SpainSchool of Engineering, University of Almeria, 04120 Almeria, SpainSchool of Engineering, University of Almeria, 04120 Almeria, SpainNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaReal-time estimation of fish biomass plays a crucial role in real-world fishery production, as it helps formulate feeding strategies and other management decisions. In this paper, a dense fish counting network called Swin-CSRNet is proposed. Specifically, the VGG16 layer in the front-end is replaced with the Swin transformer to extract image features more efficiently. Additionally, a squeeze-and-excitation (SE) module is introduced to enhance feature representation by dynamically adjusting the importance of each channel through “squeeze” and “excitation”, making the extracted features more focused and effective. Finally, a multi-scale fusion (MSF) module is added after the back-end to fully utilize the multi-scale feature information, enhancing the model’s ability to capture multi-scale details. The experiment demonstrates that Swin-CSRNet achieved excellent results with MAE, RMSE, and MAPE and a correlation coefficient R<sup>2</sup> of 11.22, 15.32, 5.18%, and 0.954, respectively. Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. Therefore, the proposed method not only counts the number of fish with higher speed and accuracy but also contributes to advancing the automation of aquaculture.https://www.mdpi.com/2077-1312/12/10/1823aquaculturefish countingdensity mapSwin transformer |
| spellingShingle | Jintao Liu Alfredo Tolón-Becerra José Fernando Bienvenido-Barcena Xinting Yang Kaijie Zhu Chao Zhou Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture Journal of Marine Science and Engineering aquaculture fish counting density map Swin transformer |
| title | Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture |
| title_full | Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture |
| title_fullStr | Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture |
| title_full_unstemmed | Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture |
| title_short | Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture |
| title_sort | hybrid swin csrnet a novel and efficient fish counting network in aquaculture |
| topic | aquaculture fish counting density map Swin transformer |
| url | https://www.mdpi.com/2077-1312/12/10/1823 |
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