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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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通过视觉和传感器数据融合和深度学习,将鱼类活动和度联系起来.

Mohammad Jahanbakht1, Andrea Tiernan2, Alzayat Saleh1

  • 1College of Science and Engineering, James Cook University, Townsville, QLD, 4814, Australia; Centre for AI and Data Science Innovation, James Cook University, Townsville, QLD, Australia.

Marine pollution bulletin
|December 2, 2025
PubMed
概括

这项研究使用深度学习来监测水下环境,准确估计水的度和检测鱼类. 调查结果显示,鱼群和水质之间存在很强的相关性,有助于海洋生态系统管理.

关键词:
深度学习是一种深度学习.鱼类检测器 鱼类检测器图像和传感器的融合.水监测 水监测 水监测度估计 度估计

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科学领域:

  • 海洋生物学 海洋生物学
  • 环境监测环境监测环境监测
  • 人工智能的人工智能是人工智能.

背景情况:

  • 水下监测对于工业的可持续性和环境的遵守至关重要.
  • 整合成像和水质传感具有挑战,原因是数据同步问题.

研究的目的:

  • 开发和整合深度学习模型,用于水下鱼类检测和水度估计.
  • 分析鱼群和港口环境中的水度之间的相互作用.
  • 展示先进技术在生态研究中的应用.

主要方法:

  • 在麦凯港部署了基于IP的水下摄像头和水质传感器.
  • 开发了一个定制的卷积神经网络 (CNN),用于基于图像的度估计 (NTU).
  • 使用基于YOLOWorld的提示式对象探测器来检测鱼类,评估YOLOWorld-v1 大.

主要成果:

  • 使用YOLOWorld-v1 Large没有训练,实现了89.7%的鱼类检测准确度.
  • 度估计的CNN模型产生了1.6NTU的根平均平方误差.
  • 确定了鱼类数量和水度之间的非线性相关性 (R2=0.93).

结论:

  • 深度学习模型有效地估计度并检测鱼类,克服同步问题.
  • 这项研究证实了鱼类丰富度和水质之间的复杂关系.
  • 这项技术为海洋生态系统的自动实时环境监测和预警系统提供了潜力.