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Buoyancy and Stability for Submerged and Floating Bodies01:11

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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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相关实验视频

Updated: May 3, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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使用EL-YOLO算法进行智能船舶物体检测的简化方法.

Defu Yang1, Mahmud Iwan Solihin2, Igi Ardiyanto3

  • 1Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia. 1002266257@ucsiuniversity.edu.my.

Scientific reports
|July 2, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了EL-YOLO,这是一种高效的轻量级物体检测算法,用于智能船只. 它提高了准确性,并在具有挑战性的海上环境中降低了计算负载.

关键词:
改进了YOLOv8的功能智能飞船是一艘智能飞船.轻量级的YOLOv8可以轻量化.修改的界限框回归修改的界限框回归对象检测检测对象检测对象检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 海洋技术 海洋技术

背景情况:

  • 在复杂的环境 (波浪,反射) 中,由于低质量的特征,海上物体检测面临挑战.
  • 现有的YOLOv8探测器在智能船只的功能理解和计算效率方面扎.
  • 无人智能飞船需要轻量级,具有成本效益的物体检测硬件.

研究的目的:

  • 开发一个高效的轻量级你只看一次 (EL-YOLO) 算法,用于智能船舶物体检测.
  • 改进海上物体的界限框回归和特征分析.
  • 创建一个精简和轻量级的网络架构,以提高计算效率.

主要方法:

  • 基于YOLOv8.8的提议EL-YOLO算法
  • 嵌入了充足的智能IOU (AWIoU) 用于界限框回归.
  • 使用快捷方式的多子子 (SMFN) 进行特征分析和贪驱动的过器修剪 (GDFP) 进行网络简化.

主要成果:

  • EL-YOLO在检测精度和轻量特性方面取得了显著的进步.
  • 使用RGB摄像头实现了智能船舶物体检测的卓越性能.
  • 与海上场景中的标准YOLOv8模型相比,显示了显著的改进.

结论:

  • 在智能航运中,EL-YOLO为高效准确的物体检测提供了一个有前途的解决方案.
  • 该算法有效地应对复杂的海上环境中的挑战.
  • 对于海上物体检测应用,EL-YOLO提供了一种轻量级但高性能的替代方案.