基于深度学习的火灾风险检测在建筑工地
1Vibration Engineering Section, Faculty of Environment, Science, and Economics, University of Exeter, Exeter EX4 4QF, UK.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
计算机视觉技术可以通过识别点火源和可燃材料来主动检测施工现场的火灾风险. 对于这一关键的防火任务,Yolov5深度学习模型表现出了卓越的性能和学习效率.
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:
- 韩国建筑工地的大规模火灾需要先进的火灾风险检测.
- 目前的方法缺乏预防火灾事件的主动能力.
研究的目的:
- 利用计算机视觉开发一个主动的火灾风险检测系统.
- 为了确定点火源 (火花) 和可燃材料 (尿泡,聚烯泡) 的共存.
主要方法:
- 对象检测应用于监控摄像头图像.
- 韩国建筑工地火灾数据的统计分析.
- 深度学习模型Yolov5和EfficientDet.的比较
主要成果:
- 约洛夫5模型实现了从87%到90%的平均精度 (mAP).
- 效率Det模型实现了mAP从82%到87%.
- 约洛夫5在学习速度和易度方面表现出优势.
结论:
- 计算机视觉,特别是Yolov5,为建筑工地主动检测火灾风险提供了可行的解决方案.
- 通过改进标签和远距离物体检测,可以提高系统的有效性.
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