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Free-falling Bodies: Example01:05

Free-falling Bodies: Example

An object falling without any air resistance under the influence of gravitational force is said to be in free-fall. For free-falling bodies, the acceleration due to gravity is constant, irrespective of their mass. Free-fall is experienced not only by objects falling downward, but also by all objects whose motion is influenced by gravitational force alone. The dynamics of free-fall motion can be calculated using kinematic equations of motion, since free-fall acceleration is constant.
The...

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相关实验视频

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Design and Analysis for Fall Detection System Simplification
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一个轻量级的人类摔倒检测网络

Xi Kan1, Shenghao Zhu2, Yonghong Zhang1,2

  • 1School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括
此摘要是机器生成的。

一个新的轻量级摔倒检测模型,CGNS-YOLO,提高了老年人的准确性和效率. 它减少了模型大小和计算负载,提高了硬件适应性,用于现实世界的落检测系统.

关键词:
在 GDCN 模块中使用 GDCN 模块.这是一个GSConv模块.这个名字是NAM NAM.这就是SIOUU的意思.这是YOLOv5的.落检测系统 落检测系统 落检测系统

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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
  • 老年学是一门学科.

背景情况:

  • 人口老龄化带来了增加的健康风险,老年人主要担心的是跌倒.
  • 现有的YOLOv5跌落检测方法在计算需求,硬件集成和阻塞处理方面面临挑战.

研究的目的:

  • 为老年人开发一种轻量级和高效的人类摔倒检测模型.
  • 为了解决目前基于YOLOv5的跌落探测系统的局限性.

主要方法:

  • 推出了CGNS-YOLO,一种新的轻量级方法,通过GSConv和GDCN模块重新配置YOLOv5s子网络.
  • 集成了一个基于规范化的注意模块 (NAM),以专注于突出的特征.
  • 采用SIoU损失函数,以提高收度和精度.

主要成果:

  • 与标准YOLOv5s相比,检测准确度提高了1.2%.
  • 模型参数数量减少了20.3%,浮点运算减少了29.6%.
  • 通过实例分析和比较评估,证明了卓越的有效性和硬件适应性.

结论:

  • CGNS-YOLO为有效和高效的老年人摔倒检测提供了一个有前途的解决方案.
  • 该模型的轻量级设计提高了它在现实世界中硬件部署的适用性.