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相关概念视频

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Updated: May 13, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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使用深度学习算法,使用无人驾驶飞行器对不安全的建筑工地条件的调查.

Sourav Kumar1, Mukilan Poyyamozhi1, Balasubramanian Murugesan1

  • 1Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括

本研究使用带有更快R-CNN的无人机 (UAV) 来监测建筑工人.

关键词:
无人驾驶飞行器无人驾驶飞行器自动检测自动检测自动检测图像识别功能 图像识别功能对象检测检测对象检测对象检测张量流的张量流是指张量流的不安全的场地条件 不安全的场地条件

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科学领域:

  • 建筑工程安全 建筑工程安全
  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉

背景情况:

  • 无人驾驶飞行器 (UAV) 越来越多地被用于安全和监控的建设.
  • 从高处落和物体落是建筑业的主要风险.
  • 确保个人防护设备 (PPE) 符合,就像头盔一样,对于工人的安全至关重要.

研究的目的:

  • 利用无人机技术,提高建筑业的劳动安全.
  • 开发一个实时监控建筑工人使用头盔的系统.
  • 通过自动化安全检查来减少受伤和死亡的风险.

主要方法:

  • 开发一个与Faster R-CNN模型和TensorFlow集成的无人机系统.
  • 实时检测和识别带头盔和没有头盔的工人.
  • 实施一个警报系统,以便立即反和干预.

主要成果:

  • 无人机系统实现了高精度 (93.1%),回忆和处理速度 (27 FPS).
  • 更快的R-CNN在各种场地条件下展示了精确的工人检测和头盔合规性.
  • 自动化安全检查减少了监督员的工作量,提高了监控效率.

结论:

  • 无人机技术为提高建筑工地安全提供了可靠且具有成本效益的解决方案.
  • 该系统提高了安全合规性,并通过确保个人防护设备的使用来保护工人.
  • 这种方法显著提高了建筑行业的整体安全管理质量.