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使用机器学习进行紧急地板图数字化.

Mohab Hassaan1, Philip Alexander Ott1, Ann-Kristin Dugstad1

  • 1Chair of Computational Modeling and Simulation, Technical University of Munich, 80333 Munich, Germany.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用人工智能的新方法,用于数字化应急地板图. 开发的系统准确地识别紧急符号,并增强建筑信息建模 (BIM) 疏散工具.

关键词:
应急地板计划 应急地板计划速度更快的R-CNN机器学习是机器学习.对象检测检测对象检测对象检测综合数据 综合数据

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 建筑安全工程 建筑安全工程

背景情况:

  • 特殊用途和高层建筑在紧急情况下会带来疏散挑战.
  • 关于在室内应急场景中使用自动驾驶汽车的研究有限.
  • 有效的应急响应需要准确和可访问的建筑信息.

研究的目的:

  • 开发一种方法来对应急符号进行分类,并将它们定位在地图上.
  • 从紧急地板图中提取几何和语义数据以进行数字化.
  • 增强基于建筑信息建模 (BIM) 的疏散工具.

主要方法:

  • 利用色彩过,聚类和对象检测来提取建筑墙壁.
  • 通过整合几何和语义数据生成干净的数字化地图.
  • 在真实和合成数据集上训练了两个基于Faster区域的卷积神经网络 (Faster R-CNNs).

主要成果:

  • 与标准模型相比,合成模型在识别罕见的紧急符号方面表现优越.
  • 该框架成功地将应急地板图数字化,提高了数据质量.
  • 更快的R-CNN模型在符号分类和定位方面实现了高精度.

结论:

  • 开发的框架有效地将紧急地板图数字化,以改进疏散规划.
  • 增强的BIM工具可以利用这些数字化数据来更好地规划路径和决策.
  • 该方法为推进数字疏散应用提供了有价值的解决方案.