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相关实验视频

Updated: Jul 2, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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基于骨架的活动识别用于通过时空图形卷积网络的隐藏工作的基于过程的质量控制.

Lei Xiao1, Xincong Yang2, Tian Peng3

  • 1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
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本研究介绍了使用空间时间图形卷积网络 (ST-GCNs) 进行实时施工质量控制的计算机视觉框架. 该模型准确地识别了石膏活动及其顺序,从而能够检测缺失或错位的步骤.

科学领域:

  • 建筑工程与管理工程与管理
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 计算机视觉 (CV) 自动化了施工现场监控,但在以过程为基础的质量控制中未得到充分利用,特别是在隐蔽工作中.
  • 目前的方法缺乏实时,自动分析施工序列和质量遵守.
  • 需要先进的技术来确保隐藏的建筑工艺的质量.

研究的目的:

  • 开发和验证使用空间时间图形卷积网络 (ST-GCNs) 在建筑中基于过程的质量控制框架.
  • 为了实现建筑活动的自动识别和质量评估的时间顺序.
  • 解决CV应用中的差距,以实时控制隐藏建筑物的质量.

主要方法:

  • 开发了一个利用空间时间图卷积网络 (ST-GCNs) 的框架.
  • 为了实验验证,收集了现场石膏工作视频数据集.
  • 该ST-GCN模型被训练来识别四种主要的石膏活动及其序列.

主要成果:

  • ST-GCN模型在验证集上识别石膏活动时达到99.48%的准确性.
  • 该模型在测试视频中成功识别了正确的活动序列,缺失的活动 (例如,玻璃纤维网格覆盖) 和不正确的活动顺序.
  • 活动订单识别是有效的,允许方便地判断过程完整性.
关键词:
在ST-GCN中.活动识别活动识别.建设建设建设建设建设.管理进度管理的进展质量控制质量控制质量控制

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结论:

  • 开发的ST-GCN框架为建设中的积极,实时,基于过程的质量控制提供了一个有希望的方法.
  • 这项技术可以通过自动化序列和活动验证,显著提高隐藏作品的质量保证.
  • 该研究表明,先进的CV技术有潜力改善施工过程监测和缺陷检测.