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通过增强的卷积神经网络和轮技术提高火灾检测准确度.

Abror Shavkatovich Buriboev1,2, Khoshim Rakhmanov3, Temur Soqiyev4

  • 1School of Computing, Department of AI-Software, Gachon University, Seongnam-si 13306, Republic of Korea.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了一种使用轮分析和深度卷积神经网络 (CNN) 的新型火灾检测方法. 先进的CNN模型实现了高精度,优于现有方法,用于增强安全和安全应用.

关键词:
这是一个CNN模特,CNN模型.轮分析 轮分析火灾检测系统的火灾检测系统.火焰识别系统的火焰识别功能

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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
  • 消防安全工程 消防安全工程

背景情况:

  • 传统的火灾检测方法经常与小型火灾和复杂的环境条件作斗争.
  • 现有的深度学习模型可能无法充分应对复杂的火灾特征和各种场景所带来的挑战.

研究的目的:

  • 开发和评估一个新的火灾检测系统,将轮分析与深度卷积神经网络 (CNN) 集成在一起.
  • 提高火灾检测的准确性和稳定性,特别是在涉及小型火灾和复杂环境的具有挑战性的场景中.

主要方法:

  • 开发了一种结合轮分析用于形状检测和深度CNN用于颜色属性分析的新方法.
  • 创建了一个定制标记的数据集,包括小火灾实例和复杂的场景,并选择了感兴趣的区域 (ROI) 来改进模型培训.
  • 增强的CNN模型经过训练,并根据各种指标进行评估.

主要成果:

  • 这种新的方法实现了高性能指标:准确率为99.4%,精度为99.3%,回忆率为99.4%,F1得分为99.5%.
  • 与以前的CNN模型相比,改进的CNN模型表现出优越的性能和先进的方法,如扩展CNN,更快的R-CNN和ResNet.
  • 该方法在所有评估指标中显示出显著的改进.

结论:

  • 拟议的轮分析和深度CNN方法为火灾检测提供了高度有效的解决方案.
  • 这种方法证明了各种安全和安全应用在各种环境中的巨大潜力,包括家庭,企业,工业场所和户外环境.