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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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在水产养殖中使用红外成像和多模式深度学习进行成本有效的鱼类体积估计.

Like Zhang1, Yanling Han1, Ge Song1

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种低成本的红外摄像头系统,用于在水产养殖中准确估计鱼类体积. 创新的管道使可扩展的生物质监测成为可能,支持可持续的海鲜生产.

关键词:
水产养殖的水产养殖生物质监测 生物质监测具有成本效益,具有成本效益.估计鱼的数量 估计鱼的数量红外成像技术 红外成像技术多模式深度学习多模式深度学习合成数据的生成.

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相关实验视频

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科学领域:

  • 水产养殖技术 水产养殖技术
  • 计算机视觉 计算机视觉 计算机视觉
  • 生物质估计生物质估计

背景情况:

  • 准确的鱼体量估计对于可持续的水产养殖至关重要,但传统方法具有侵入性和成本.
  • 现有的非侵入性技术往往需要昂贵的多传感器系统,限制了可扩展性.
  • 需要具有成本效益的,非侵入性的解决方案来实时监测密集水产养殖水库中的生物质.

研究的目的:

  • 开发一个具有成本效益的仅红外 (IR) 管道,用于从低成本的红外视频中重建深度和红绿蓝 (RGB) 数据.
  • 为了在密集的水产养殖环境中实现可扩展和准确的鱼类生物量监测.
  • 为了降低鱼类体积估计的硬件成本,同时保持高精度.

主要方法:

  • 开发了一个只有IR的管道,包含五个集成模块:IR到深度估计,IR到RGB生成,检测和跟踪,实例细分和体积估计.
  • 利用了轮引导注意力,纹理条件注入,交叉模式融合,深度引导分支和轨道深度变压器融合.
  • 包含特定损失,如光滑损失,水适应性损失和变形适应性损失,以提高性能.
  • 在124个视频中的166条金鱼的数据集上训练了系统,每箱8-16条鱼.

主要成果:

  • 获得了0.85cm3的平均绝对误差 (MAE) 和0.961的确定系数 (R2) 来估计鱼体积.
  • 在精确度上,超越了最先进的方法,精度为19-41%.
  • 与现有的多传感器设置相比,硬件成本降低了80%.
  • 在密集的水箱条件下表现出强性,每个水箱有8-16只鱼.

结论:

  • 拟议的IR-only管道为水产养殖中鱼类体积估计和生物质监测提供了具有成本效益和准确的解决方案.
  • 这项技术推进了精密水产养殖,使得料优化和健康监测更好.
  • 该系统通过支持有效的资源管理来促进环境可持续性,以应对全球海鲜需求的不断增长.