In this paper, a new monitoring alert system for air pollution emergencies is proposed. The proposed system can perform air quality monitoring to provide real-time alerts of an individual event. The system uses two image analysis techniques, namely pixel recognition and haze extraction, for video fire smoke detection. The image analysis process is divided into daytime and nighttime image analyses, which involve the analysis of red-green-blue (RGB) and gray scale images. The images analyzed in this study were captured by the video camera of an air quality monitoring station. Seven fire accidents around a selected industrial park and downtown area were analyzed in detail. Among these accidents, three occurred at daytime, one occurred over 7 days, and three occurred at nighttime. Alert models based on pixel recognition and haze extraction were established. These models incorporated the formulas of haze equivalent (H_T(t)) and separated pixels (X_T(t)), as well as the threshold equations of haze equivalent (∇_H) and separated pixels (∇_X). An alert signal is sent to the administrator when H_T(t) > ∇_H or X_T(t) > ∇_X. The obtained results indicate that a real-time observation and alert system based on two image analysis techniques can be designed for air quality monitoring without expensive hardware devices. This alert system can be used by administrators to understand the course of a reportable event, especially as evidence for the appraisal of fire accidents. It is recommended that this system be connected to the fire brigades in order to obtain early fire information.

中文翻译：

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基于两种图像分析技术的可报告火灾事件综合空气质量监测和预警系统

本文提出了一种新的空气污染应急监测预警系统。所提出的系统可以执行空气质量监测以提供单个事件的实时警报。该系统使用两种图像分析技术（即像素识别和雾度提取）来进行视频火灾烟雾检测。图像分析过程分为白天和夜间图像分析，其中涉及红绿蓝（RGB）和灰度图像的分析。在这项研究中分析的图像是由空气质量监测站的摄像机捕获的。详细分析了选定的工业园区和市区附近的七起火灾事故。在这些事故中，三起发生在白天，一起发生在7天之内，三起发生在夜间。建立了基于像素识别和雾度提取的预警模型。这些模型结合了雾度当量公式（ħ _Ť（吨））和分离的像素（X _Ť（吨）），以及雾度当量（∇的阈值方程_ħ）并分离像素（∇ _X）。警报信号被发送给管理员时ħ _Ť（吨）>∇ _ħ或X _Ť（吨）>∇ _X。获得的结果表明，可以将基于两种图像分析技术的实时观察和警报系统设计为无需昂贵的硬件设备即可进行空气质量监测。管理员可以使用此警报系统来了解可报告事件的过程，尤其是作为火灾事故评估的依据。建议将此系统连接到消防队，以获得早期火灾信息。