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Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

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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: Sep 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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先进的深度学习框架用于用多束前声纳探测水下物体.

Liangfu Ge1, Premjeet Singh2, Ayan Sadhu1

  • 1Department of Civil and Environmental Engineering, The Western Academy for Advanced Research, Western University, London, ON, Canada.

Structural health monitoring
|July 7, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了使用声纳数据进行水下物体检测 (UOD) 的先进深度学习框架. 基于YOLOv7的增强模型显著改善了水下基础设施管理的目标分类,本地化和转移学习.

关键词:
水下基础设施检查水下基础设施检查深度学习是一种深度学习.多光束前声纳成像多光束前声纳成像远程操作的车辆远程操作的车辆结构健康监测 结构健康监测转移学习转移学习水下物体检测系统是用来检测水下物体的.

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科学领域:

  • 机器人技术和自主系统
  • 计算机视觉 计算机视觉
  • 海洋工程 海洋工程

背景情况:

  • 水下物体检测 (UOD) 对于基础设施维护和资产管理至关重要.
  • 在具有挑战性的水下条件下,声纳成像优先于光学方法.
  • 现有的基于声纳的UOD方法在低分辨率和差差对比度方面扎,限制了精度和可转移性.

研究的目的:

  • 开发一个先进的深度学习框架,以使用多束前声纳数据改进UOD.
  • 解决当前基于声纳的物体检测算法的精度和可转移性挑战.

主要方法:

  • 为声纳数据调整了YOLOv7网络架构.
  • 在数据预处理,功能融合和丢失函数中实现了独特的优化.
  • 在公共数据集上验证了框架,并通过与水下遥控车辆的实验进行了验证.

主要成果:

  • 与现有的基于声纳的方法相比,拟议的框架展示了优越的对象分类性能.
  • 在目标分类,本地化和转移学习能力方面取得了重大改进.
  • 实验验证证了该框架在水下远程操作车辆上的有效性.

结论:

  • 先进的深度学习框架为使用声纳数据的UOD提供了显著的改进.
  • 该框架显示了潜水结构检查,监测和自主资产管理的潜力,因为其增强的能力.
  • 这项工作促进了深度学习在海洋机器人和基础设施管理中的应用.