基于普通CCD摄像机的火灾探测技术的研究
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摘要
随着社会经济的发展和科学技术的进步，特别是城市人口的急剧增加和城市化进程的飞速发展，高层建筑和大空间场所越来越多。由于高层建筑火灾具有传播速度快、灭火和营救都比较困难等特点，所以如何有效的进行高层建筑火灾的安全防范以及尽早发现火灾的问题就变得越来越紧迫。作为一种新型的有效的早期火灾探测技术，图像型火灾探测技术倍受人们的关注。本文通过对数字图像处理技术的研究和讨论，设计并实现了多种检测算法和基于BP神经网络的图像型火灾探测系统。
论文系统地讨论了火灾产生的机理和其三个发展阶段及其各个阶段的特点，讨论了传统的火灾探测技术存在的缺陷并与新型火灾探测技术进行了对比，在此基础上介绍了图像型火灾探测技术的分类、使用的探测设备、图像型火灾探测系统的组成以及数字图像处理技术。
本文详细地研究了火灾图像的增强处理、噪声滤除、最大熵阈值分割、区域生长和Otsu阈值分割等算法及其在火灾图像处理中的应用。并通过将数种算法集成起来的方法，提出了一种在序列图像中跟踪火灾火焰的算法，在图像序列中提取出火焰目标。此外，本文实现了通过瞬时运动分析和累积运动分析的方法在火灾烟气图像序列中提取火灾烟气轮廓。
对于火灾图像的识别技术，我们将其分为火灾火焰图像的识别技术和火灾烟雾图像的识别技术两类，重点讨论了火灾火焰图像的识别技术。本文详细地分析了火灾火焰和其它一些干扰现象的面积变化规律、形体变化规律、边缘变化规律、闪烁频率规律和整体移动趋势，并且针对于每一种规律都提出相应的图像识别算法。通过这些算法，我们可以识别出早期火灾的火焰图像并能够区别其它的干扰现象。本文介绍了火灾烟气的光学特性，在此基础上提出了识别火灾烟气轮廓的算法。
哈尔滨工程大学硕士学位论文
在论文的最后，讨论了基于神经网络的火灾探测方案。首先介绍了神经
网络的基本概念，接着给出了神经网络的具体结构和输入输出单元的设计方
案。在此基础上，我们设计了基于BP神经网络的火灾探测系统，并使用一
系列的火灾图像样本和干扰模式的图像进行了实验。实验结果表明，该神经
网络火灾探测系统具有良好的抗干扰能力。
With the development of social economy and the progress of technology, especially the sharp increase of the city population and fast growth of the citification, there have been more and more high buildings and large space. The high buildings have such features as fire spreading fast, difficulty of extinguishing a fire and rescuing human beings and properties, so it has been an increasing urgent demand that how to effectively protect the safety of high building and detect the fire as early as possible. As a new-style and effective measure for the detection of early fire, much attention has been focused on image fire detection technology. In this dissertation, by the aid of digital image processing techniques, several detecting algorithms and the image fire detection systems based on the nerve net have been designed and realized.
The principle of fire occurring, three developing stages and characters of every stage have been discussed systematically in this dissertation. The difference is also studied between conventional fire detection systems and new-style fire detection techniques. The classification of image fire detection, detection devices, the composition of the fire detection system and basic principle of digital image processing have been introduced in detail as well.
The algorithms of fire image enhancement, noise filtering, the maximum entropy threshold segmentation, region growth, Otsu threshold segmenting and their applications in the fire image processing have been studied in this dissertation. On the base of integration of several segmenting algorithms, a new method has been put forward that can track the fire flame in the fire image sequence and can obtain the fire target. In addition, the algorithm has been discussed and realized that distills the fire smoke envelope in fire smoke image
sequence by use of instantaneous motion analysis and cumulated motion analysis. For the fire image recognition, the fire flame image recognition and fire smoke image recognition have been discussed respectively. Stress has been put on the flame image recognition. The features of fire flame and other disturbing phenomena such as area variety, shape variety, edge variety, flame flicker frequency and the whole motion trend have been detailed studied in this dissertation. And the corresponding recognition algorithms have been given. So the early fire flame can be recognized and the disturbing phenomena can be distinguished. The algorithm how to recognize the fire smoke image on the base of the analysis of optical characters of fire smoke has been also discussed.
The fire detecting scheme based on nerve net has been discussed in the last
part of this dissertation. First, the basic concept of nerve net has been introduced.
And then the detail structure of the BP nerve net and the detail design precept of input and output layer have been given. Finally the fire detection system based on
BP nerve net has been realized. The experiment results show that the BPNN fire detection system has good feature of robustness.
论文系统地讨论了火灾产生的机理和其三个发展阶段及其各个阶段的特点，讨论了传统的火灾探测技术存在的缺陷并与新型火灾探测技术进行了对比，在此基础上介绍了图像型火灾探测技术的分类、使用的探测设备、图像型火灾探测系统的组成以及数字图像处理技术。
本文详细地研究了火灾图像的增强处理、噪声滤除、最大熵阈值分割、区域生长和Otsu阈值分割等算法及其在火灾图像处理中的应用。并通过将数种算法集成起来的方法，提出了一种在序列图像中跟踪火灾火焰的算法，在图像序列中提取出火焰目标。此外，本文实现了通过瞬时运动分析和累积运动分析的方法在火灾烟气图像序列中提取火灾烟气轮廓。
对于火灾图像的识别技术，我们将其分为火灾火焰图像的识别技术和火灾烟雾图像的识别技术两类，重点讨论了火灾火焰图像的识别技术。本文详细地分析了火灾火焰和其它一些干扰现象的面积变化规律、形体变化规律、边缘变化规律、闪烁频率规律和整体移动趋势，并且针对于每一种规律都提出相应的图像识别算法。通过这些算法，我们可以识别出早期火灾的火焰图像并能够区别其它的干扰现象。本文介绍了火灾烟气的光学特性，在此基础上提出了识别火灾烟气轮廓的算法。
哈尔滨工程大学硕士学位论文
在论文的最后，讨论了基于神经网络的火灾探测方案。首先介绍了神经
网络的基本概念，接着给出了神经网络的具体结构和输入输出单元的设计方
案。在此基础上，我们设计了基于BP神经网络的火灾探测系统，并使用一
系列的火灾图像样本和干扰模式的图像进行了实验。实验结果表明，该神经
网络火灾探测系统具有良好的抗干扰能力。
With the development of social economy and the progress of technology, especially the sharp increase of the city population and fast growth of the citification, there have been more and more high buildings and large space. The high buildings have such features as fire spreading fast, difficulty of extinguishing a fire and rescuing human beings and properties, so it has been an increasing urgent demand that how to effectively protect the safety of high building and detect the fire as early as possible. As a new-style and effective measure for the detection of early fire, much attention has been focused on image fire detection technology. In this dissertation, by the aid of digital image processing techniques, several detecting algorithms and the image fire detection systems based on the nerve net have been designed and realized.
The principle of fire occurring, three developing stages and characters of every stage have been discussed systematically in this dissertation. The difference is also studied between conventional fire detection systems and new-style fire detection techniques. The classification of image fire detection, detection devices, the composition of the fire detection system and basic principle of digital image processing have been introduced in detail as well.
The algorithms of fire image enhancement, noise filtering, the maximum entropy threshold segmentation, region growth, Otsu threshold segmenting and their applications in the fire image processing have been studied in this dissertation. On the base of integration of several segmenting algorithms, a new method has been put forward that can track the fire flame in the fire image sequence and can obtain the fire target. In addition, the algorithm has been discussed and realized that distills the fire smoke envelope in fire smoke image
sequence by use of instantaneous motion analysis and cumulated motion analysis. For the fire image recognition, the fire flame image recognition and fire smoke image recognition have been discussed respectively. Stress has been put on the flame image recognition. The features of fire flame and other disturbing phenomena such as area variety, shape variety, edge variety, flame flicker frequency and the whole motion trend have been detailed studied in this dissertation. And the corresponding recognition algorithms have been given. So the early fire flame can be recognized and the disturbing phenomena can be distinguished. The algorithm how to recognize the fire smoke image on the base of the analysis of optical characters of fire smoke has been also discussed.
The fire detecting scheme based on nerve net has been discussed in the last
part of this dissertation. First, the basic concept of nerve net has been introduced.
And then the detail structure of the BP nerve net and the detail design precept of input and output layer have been given. Finally the fire detection system based on
BP nerve net has been realized. The experiment results show that the BPNN fire detection system has good feature of robustness.
引文
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[2] 董华，程晓舫等．基于图像探测的早期火灾烟气模式研究．光学技术．1999，28(1)：60-63页
[3] Sahoo P K, Wang A K C, Chen Y C. A survey of thresholding technique. Computer Vision, Graphics, and Image Processing. 1988, 41: 233-260P
[4] 罗希平，田捷等．图像分割方法综述．模式识别与人工智能．1999，12(3)：300-312页
[5] 张毅军，吴雪菁等．二维熵图像分割的快速递推算法．模式识别与人工智能．1997，10(3)：260-264页
[6] 付忠良．图像阈值选取方法-Otsu方法的推广．计算机应用．2000，20(5)：37-39页
[7] R.F. Richards, B. N. Munk, O. A. Plumb. Fire detection, location and heat release rate through inverse problem solution..Part Ⅰ: Theory. Fire Safety Journal. 1997, 28: 323-350P
[8] R.F. Richards, B. N. Munk, O. A. Plumb. Fire detection, location and heat release rate through inverse problem solution. Part Ⅱ: Experiment. Fire Safety Journal. 1997, 28: 351-378P
[9] 杨宏文，唐晓军等．人工神经网络用于燃烧火焰状念识别的研究．工程热物理学报．1997，18(4)：521-524页
[10] 吴龙标，宋卫国．液晶光闸在火灾探测中的应用．光学技术．1997，1(1)：4-6页
[11] Song Weiguo, Wu Longbiao, Lu Jiecheng. The study of the image fire detection method. International Conference of Safety Science and Technology98, Bei Jing, China, Oct. 1998
[12] 邓志华，杨立中等．火灾早期特性分析及影像特征研究．火灾科学．1997，6(2)：81-85页
[13] 吴龙标，宋卫国，卢结成．图像火灾监控中的一个新颖的火灾判据．自
然科学进展．2001，11(1)：60-66页
[14] 袁宏永，苏国锋等．基于实时序列红外图像的火灾频闪频率测量研究．’99城市火灾安全国际学术会议．1999：162-166页
[15] 范维澄编著．火灾科学导论．武汉：湖北人民出版社，1993：1-262页
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