您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture

文献类型： 外文期刊

作者： Liu, Jintao 1 ; Tolon-Becerra, Alfredo 1 ; Bienvenido-Barcena, Jose Fernando 1 ; Yang, Xinting 2 ; Zhu, Kaijie 2 ; Zhou, Chao 2 ;

作者机构： 1.Univ Almeria, Sch Engn, Almeria 04120, Spain

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

4.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Beijing, Peoples R China

关键词： aquaculture; fish counting; density map; Swin transformer

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）

ISSN：

年卷期： 2024 年 12 卷 10 期

页码：

收录情况： SCI

摘要： 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 R2 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.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台