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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

Updated: Jan 15, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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鱼KP-YOLOv11:在复杂的水下环境中的鱼类大小和质量的自动估计模型.

Jinfeng Wang1, Zhipeng Cheng1, Mingrun Lin1

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Animals : an open access journal from MDPI
|October 16, 2025
PubMed
概括

本研究为复杂的水产养殖环境引入了一个非接触鱼类大小和质量估计框架. 该系统准确地测量了鱼的尺寸和体重,改善了水产养殖管理.

关键词:
计算机视觉 计算机视觉鱼的质量估计.鱼类大小估计 鱼类大小估计关键点检测 关键点检测立体视觉视觉的立体视觉

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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
  • 生物识别信息 生物识别信息

背景情况:

  • 准确的鱼类大小和质量估计对于水产养殖管理至关重要.
  • 现有的方法受到理想条件要求的限制,阻碍了现实世界的应用.
  • 复杂的水下环境给非接触式测量带来了挑战.

研究的目的:

  • 开发一个强大的非接触框架,用于在复杂的水下条件下估计鱼的尺寸和质量.
  • 提高鱼类测量技术在实际水产养殖环境中的准确性和适用性.

主要方法:

  • 集成了一个改进的FishKP-YOLOv11模块 (基于YOLOv11),用于关键点检测.
  • 利用立体视觉技术从二维检测中重建3D鱼类关键点坐标.
  • 应用随机森林模型来确定鱼类大小和质量之间的关系.

主要成果:

  • 与各种YOLO版本相比,FishKP-YOLOv11模块表现出卓越的性能 (mAP).
  • 长度,宽度和质量估计的平均绝对误差 (MAE) 分别为0.35厘米,0.10厘米和2.7克.
  • 该框架在复杂的水下环境中实现了高精度.

结论:

  • 拟议的框架在具有挑战性的水产养殖环境中对非接触鱼的尺寸和质量估计是有效的.
  • 该系统为实时水产养殖管理和监测提供了一个实用的解决方案.
  • 该研究验证了该框架适用于实际鱼类繁殖场景的适用性.