基于多传感器融合和轻量级卷积神经网络的室内火灾检测方法.
Xinwei Deng1, Xuewei Shi1, Haosen Wang2
1Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
早期室内火灾检测至关重要. 一种新方法使用多传感器融合和轻型卷积神经网络 (CNN) 来实现嵌入式系统的99.1%准确性,最大限度地降低计算成本.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 安全科学安全科学 安全科学
背景情况:
- 室内火灾在全球造成重大伤亡和经济损失.
- 准确和早期的室内火灾检测对于减轻这些威胁至关重要.
- 资源有限的嵌入式平台需要有效的检测方法.
研究的目的:
- 为资源有限的嵌入式平台提出准确和高效的室内火灾检测方法.
- 为了利用多传感器融合和轻量级卷积神经网络 (CNN).
- 为了提高嵌入式火灾检测系统的稳定性和性能.
主要方法:
- 应用的萨维茨基-戈莱 (SG) 波器用于异质的传感器数据清理.
- 利用格拉米安角场 (GAF) 将时间序列数据转换为矩阵.
- 将传感器数据集成到3D矩阵中,保持时间依赖.
- 通过减少网络块,频道和层,开发了一个轻量级的CNN.
- 使用火力动态模拟器 (FDS) 进行数据模拟,以提高网络的稳定性.
主要成果:
- 在实验验证中达到99.1%的令人印象深刻的准确性.
- 拟议的轻量级CNN展示了少量的参数和低计算要求.
- 该方法非常适合资源有限的嵌入式平台.
结论:
- 拟议的多传感器融合和轻量级CNN方法为室内早期火灾检测提供了高度准确和高效的解决方案.
- 这种方法有效地解决了资源有限的嵌入式系统的局限性.
- 该方法通过及时可靠地检测火灾来提高安全性.
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