级联深度学习用于火焰检测和火灾安全中释放热量的量化
Shiqiang Deng1, Shuping Jiang1,2, Meng Yang2,3
1School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.
Scientific reports
|October 24, 2025
概括
这项研究引入了一个新的深度学习框架,用于准确,实时测量火灾安全中的热释放率 (HRR). 该系统将视觉火焰检测与热力学分析相结合,用于高效的火灾风险评估.
科学领域:
- 消防安全工程 消防安全工程
- 人工智能的人工智能
- 热力学是一种热力学.
背景情况:
- 准确的实时热释放率 (HRR) 测量对于有效的消防安全工程至关重要.
- 在动态火灾场景中,现有的方法可能缺乏效率或准确性.
研究的目的:
- 开发和验证一个级联深度学习框架,用于非接触式实时HRR测量.
- 将视觉火焰检测与热力学分析相结合,以改善火灾风险评估.
主要方法:
- 一个级联框架,结合了增强的YOLOv8n (与ECA和BiFPN) 用于火焰检测和双分支CNN用于HRR量化.
- 检测模块实现了95.2%的精度和88.3%的回忆.
- HRR模块处理了空间和频域的火焰特征,显示出高精度 (R2=0.976) 与VGG16/ResNet50.0相比,参数显著减少.
主要成果:
- 综合框架在各种火焰阶段显示出强大的性能,使用NIST火灾数据库进行验证.
- 在平均绝对误差 (MAE) 方面,HRR量化模块的性能优于视觉变压器.
- 级联方法通过激活检测后的HRR分析来优化效率,减少错误报警.
结论:
- 拟议的深度学习框架为实时HRR测量提供了一种高效准确的非接触方法.
- 这种方法为非接触式火灾风险评估建立了新的范式.
- 需要进一步验证,特别是在HRR峰值阶段.
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