主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2024, Vol. 43 ›› Issue (11): 1528-1532.

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基于人工智能图像处理的古建筑受控火干扰辨识方法

张曦1,2,3, 李晓旭1,2,3, 李泊宁1,2,3, 于春雨1,2,3   

  1. （1. 应急管理部沈阳消防研究所，辽宁 沈阳 110034；2. 辽宁省火灾防治技术重点实验室，辽宁 沈阳 110034；3. 消防与应急救援国家工程研究中心，辽宁 沈阳 110034）
  • 收稿日期:2024-01-04 修回日期:2024-03-07 出版日期:2024-11-15 发布日期:2024-11-15
  • 作者简介:张 曦（1982- ），男，应急管理部沈阳消防研究所助理研究员，主要从事火灾探测方面研究，辽宁省沈阳市皇姑区文大路218-20号，110034。
  • 基金资助:
    基金项目：应急管理部消防救援局科研计划项目（2019XFCX44）

Identification method of controlled fire disturbance in ancient buildings based on artificial intelligence image processing technology

Zhang Xi1,2,3, Li Xiaoxu1,2,3, Li Boning1,2,3, Yu Chun yu1,2,3   

  1. (1. Shenyang Fire Science and Technology Research Institute of MEM, Liaoning Shenyang 110034, China;2. Liaoning Key Laboratory of Fire Prevention Technology, Liaoning Shenyang 110034, China; 3. National Engineering Research Center of Fire and Emergency Rescue, Liaoning Shenyang 110034, China)
  • Received:2024-01-04 Revised:2024-03-07 Online:2024-11-15 Published:2024-11-15

摘要： 古建筑以木结构为主体，导致人为造成的古建筑火灾频发。古建筑中为祭拜、照明或营造氛围一般都存在大量和长时间的点火、焚烧等情况，对建筑内设置的常规点型感烟、吸气式、线型光束和图像型等火灾探测器产生严重的干扰和影响，存在误报率高的问题。因此，本文针对大型古建筑内部大空间火灾预警探测技术难题，提出一种新的基于人工智能图像处理技术的古建筑受控火干扰辨识方法，通过设计的火灾智能检测算法、受控火智能辨识与确认算法，实现了典型场景下真实火灾与受控火的“行为”图像特征智能辨识。试验结果表明，本方法可在4 s内分别对真实火灾和受控火做出预警提示，有效解决大型古建筑火灾防控技术难题。

关键词: 火灾检测, 古建筑, 受控火, 深度学习

Abstract: The use of wooden structures as the main structure in ancient buildings has led to frequent human caused fires. Due to the large and long-term ignition, burning, and other situations in ancient buildings for worship, lighting, or creating an atmosphere, it seriously interferes and affects the conventional point type smoke detectors, suction type, linear light beams, and image type fire detectors installed inside the building, resulting in a high false alarm rate. Therefore, this article proposes a new method for identifying controlled fire interference in ancient buildings based on artificial intelligence image processing technology, aiming at the technical difficulties of fire early warning and detection in large spaces inside large ancient buildings. Through the design of fire intelligent detection algorithms and controlled fire intelligent identification and confirmation algorithms, the intelligent identification of "behavior" image features of real fires and controlled fires in typical scenarios is achieved. The experimental results show that this method can provide warning prompts for both real and controlled fires within 4 s, effectively solving the technical difficulties of fire prevention and control in large ancient buildings.

Key words: fire detection, ancient architecture, controlled fire, deep learning