基于图像信息融合技术的火灾识别算法研究
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摘要
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,因此其有效防范及早期预测十分重要。针对传统火灾探测技术存在的不稳定、误判率高等缺点,本文着重分析了室内火灾图像与常见干扰光源图像的特点,并在此基础上研究了火焰图像采集、去噪、分割、边缘提取的算法,提出用火焰区域面积变化、质心位置、尖角数、圆形度等信息作为火灾判据,并用模糊神经网络对以上特征参数进行数据融合,作出火灾判断。
本文采集了大量火灾试验图片和常见干扰光源的图片用于实验分析;通过对不同的图像的分析,确定对动态图像序列采用差影法获取可疑火焰区域图像;用中值滤波法去除图像噪声;用基于灰度直方图的中间阈值法进行图像分割,并在此基础上将图像二值化。以相邻帧图像中可疑火焰区域的面积变化、质心位置随机性变化以及单幅图像中可疑火焰区域的尖角数和圆形度等形态特征对火灾进行判别。
在彩色图像分割算法的研究中,本文分析研究了现有的图像分割方法,针对最大类间方差法(Otsu法)易受图像直方图形状的限制以及分割效果不稳定等问题,提出了一种火焰图像分割的综合方法:利用HSI颜色空间参数对火焰图像建立彩色空间模型,根据火焰图像的S(饱和度)、H (颜色)等特征参数对图像进行预处理以确定火焰疑似区域,再用最大类间方差法进行阈值分割。通过实例仿真得到了轮廓清晰的分割图像。试验结果表明,该方法能够弥补最大类间方差法的局限性,扩展该方法的适用范围。
最后,论文给出了模糊神经网络的具体结构和输入输出单元的设计方案,并使用大量的火灾图像样本和干扰模式的图像进行了验证。试验结果表明,基于模糊神经网络的信息融合算法能够有效地识别出火灾火焰,并且具有良好的抗干扰能力。
For the fire’s characteristics of spreading quickly in highrise and difficulty of fighting and rscue,it is very important to detect and guard effectively in early stage. The shortcoming is also studied about conventional fire detection systems. The characteristics of fire image and familiar Interference light source have been analyzed in this dissertation, and the arithmetic has been studied about flame image acquisition, denoising, segmentation and edge extracting. The topic introduction four criterions: the area increases gradually, the centroid changes random, the taper angles, and the circularity. The Fuzzy neural network(NN) is used in Information Fusion to decide the fire.
The massive images of fire and familiar interference light source are gathered for test analysis. By analyzing different images, method of difference image is used to obtain suspicious flame region image, median value filter method is used to remove the noise of image and intermediate threshold is used to segment images and implement binarization images. The fire flame is distinguished by the morphological characters of the area change and random centroid position of suspicious flame region which are from adjacent images, as well as the circularity and taper angle of suspicious flame region in each image.
After analyzing image segmentation methods now available, to solve gray-level histogram shape limit and unsteady segmentation effect of the Otsu method, the paper provides a method of flame image segmentation which uses the Otsu method combined with the HSI color-space parameters. First build a color space model of flame image based on HSI color space parameter to preprocess the flame image using Saturation(S-parameters)、hue(H parameters) of image features, then segment the flame image according to the threshold value with the Otsu. At last, a clear profile of segmented image can be obtained by simulation. The test results show that this method can make up some shortcomings of the Otsu and widened its application range .
The detail structure of the Fuzzy NN and the detail design precept of input and output layer have been given. The experiment results show that the BPNN fire detection algorithm can recognize the fire flame effectively and has good anti-interference capability.
本文采集了大量火灾试验图片和常见干扰光源的图片用于实验分析;通过对不同的图像的分析,确定对动态图像序列采用差影法获取可疑火焰区域图像;用中值滤波法去除图像噪声;用基于灰度直方图的中间阈值法进行图像分割,并在此基础上将图像二值化。以相邻帧图像中可疑火焰区域的面积变化、质心位置随机性变化以及单幅图像中可疑火焰区域的尖角数和圆形度等形态特征对火灾进行判别。
在彩色图像分割算法的研究中,本文分析研究了现有的图像分割方法,针对最大类间方差法(Otsu法)易受图像直方图形状的限制以及分割效果不稳定等问题,提出了一种火焰图像分割的综合方法:利用HSI颜色空间参数对火焰图像建立彩色空间模型,根据火焰图像的S(饱和度)、H (颜色)等特征参数对图像进行预处理以确定火焰疑似区域,再用最大类间方差法进行阈值分割。通过实例仿真得到了轮廓清晰的分割图像。试验结果表明,该方法能够弥补最大类间方差法的局限性,扩展该方法的适用范围。
最后,论文给出了模糊神经网络的具体结构和输入输出单元的设计方案,并使用大量的火灾图像样本和干扰模式的图像进行了验证。试验结果表明,基于模糊神经网络的信息融合算法能够有效地识别出火灾火焰,并且具有良好的抗干扰能力。
For the fire’s characteristics of spreading quickly in highrise and difficulty of fighting and rscue,it is very important to detect and guard effectively in early stage. The shortcoming is also studied about conventional fire detection systems. The characteristics of fire image and familiar Interference light source have been analyzed in this dissertation, and the arithmetic has been studied about flame image acquisition, denoising, segmentation and edge extracting. The topic introduction four criterions: the area increases gradually, the centroid changes random, the taper angles, and the circularity. The Fuzzy neural network(NN) is used in Information Fusion to decide the fire.
The massive images of fire and familiar interference light source are gathered for test analysis. By analyzing different images, method of difference image is used to obtain suspicious flame region image, median value filter method is used to remove the noise of image and intermediate threshold is used to segment images and implement binarization images. The fire flame is distinguished by the morphological characters of the area change and random centroid position of suspicious flame region which are from adjacent images, as well as the circularity and taper angle of suspicious flame region in each image.
After analyzing image segmentation methods now available, to solve gray-level histogram shape limit and unsteady segmentation effect of the Otsu method, the paper provides a method of flame image segmentation which uses the Otsu method combined with the HSI color-space parameters. First build a color space model of flame image based on HSI color space parameter to preprocess the flame image using Saturation(S-parameters)、hue(H parameters) of image features, then segment the flame image according to the threshold value with the Otsu. At last, a clear profile of segmented image can be obtained by simulation. The test results show that this method can make up some shortcomings of the Otsu and widened its application range .
The detail structure of the Fuzzy NN and the detail design precept of input and output layer have been given. The experiment results show that the BPNN fire detection algorithm can recognize the fire flame effectively and has good anti-interference capability.
引文
[1]国家自然科学基金委员会工程与材料科学部.工程热物理与能源利用.北京:科学出版社,2006:237-240
[2]丁瑞金等.火灾探测器的最新发展.消防技术及产品信息,2005.12:64-66
[3]林其钊,王清安.灾害燃烧及特点.火灾科学,2000.9(4):19-24
[4]陈涛,袁宏永,范维澄.火灾探测技术研究的展望.火灾科学,2001.10(2):108-112
[5]米锐.火灾图像自动监测技术的研究与开发:[硕士学位论文].成都:四川大学,2003
[6]章毓晋.图像处理和分析.北京:清华大学出版社,1999:72-100
[7] castleman.数字图形处理.北京:电子工业出版社,2002:58-65
[8]程佩青.数字信号处理.北京:高等教育出版社,2001:37
[9]李弼程,彭天强,彭波等编著.智能图像处理技术.北京:电子工业出版社,2004:221-305
[10]章毓晋.图像处理和分析基础.北京:高等教育出版社,2002
[11]张庆磊.基于图像处理的大空间火灾探测技术研究:[硕士学位论文].成都:西华大学,2007
[12]章毓晋.图像分割.北京:科学出版社,2001:43-65
[13]吴龙标,方俊,谢启源.火灾探测与信息处理.北京:化学工业出版社,2006:99-160,243-250
[14]安丰凌,孙劲光,张新君.一种火灾报警系统中火焰区域的检测方法.现代计算机,2007.10:44-46
[15]王亚军,徐大芳,陈向成,杨圣.基于火焰图像动态特征的火灾识别算法.测控技术,2007.26(5):7-9
[16]易正俊.多源信息智能融合算法:[博士学位论文].四川:重庆大学,2002
[17]姜长生,王从庆等.智能控制与应用.北京:科学出版社,2007:92-103
[18]葛哲学,孙志强.神经网络理论与MATLAB R2007实现.北京:电子工业出版社,2007:46-55
[19]王志鹏.基于信息融合技术的故障诊断方法的研究及应用:[博士学位论文].大连:大连理工大学,2001:31-33
[20]水祺,吕景辉.火灾报警系统中的信息融合.国外电子测量技术,2005.6:39-41
[21] Derbel F, Hom M, Trankler H R. System Identification techniques for simulating fire detector signals. MTC 99,IEEE Multi-conference ,Venice Italy,1999:898-903
[22] Otsu N.A threshold selection method from gray-level Histogram.IEEE Trans Actions on SMC,1979(9):652-655.
[23] NFPA92B.Smoke Management Systems in Malls, Atria, and Large Large Areas. National Fire Association.USA,1991
[24] S.Nakanishi,et al., Intelligent Fire Warning System Using Fuzzy Technology. Journal of Japan Society for Fuzzy Theory and Systems, 1993.Vol.5(1):95-107
[25] Timon Chin-Ting and Philip M.Wolfe,Amalgamation of neural networks and logic systems-a survey. Computers & Industrial Engineering, 1995.Vol.29(1-4): 193-197
[26] G.O.Hansell, H.P.Morgan, Design Approaches for Smoke Control in Atrium Buildings, Building Research Establishment Report, 1994
[27] T. Tanaka,T.Yamana, Smoke Control in Large Scale Spaces, part 1. Fire Science & Technology,1985.Vol.5(1): 21-40
[28] D. Park, A.Kandel and G.Langholz, Genetic-Based New Fuzzy Reasoning Models with Application to Fuzzy Control,IEEE Transactions on Systems, Man, and Cybernetics, 1994.Vol.24
[29] C.C.Lee, Fuzzy logic in control systems, IEEE trans. Syst. Man Cybern, 1990.Vol.20(2):404-435
[30] Muller H.C, Fisclier A.A Robust Fire Detection Algorithm for Temperature and Optical Smoke Density using Fuzzy Logic, IEEE Congference on security Technology,1995:197-204
[31] Nonlinear neural network, Principles, mechanism, and architectures,Neural Network,1988.Vol.1:17-61
[2]丁瑞金等.火灾探测器的最新发展.消防技术及产品信息,2005.12:64-66
[3]林其钊,王清安.灾害燃烧及特点.火灾科学,2000.9(4):19-24
[4]陈涛,袁宏永,范维澄.火灾探测技术研究的展望.火灾科学,2001.10(2):108-112
[5]米锐.火灾图像自动监测技术的研究与开发:[硕士学位论文].成都:四川大学,2003
[6]章毓晋.图像处理和分析.北京:清华大学出版社,1999:72-100
[7] castleman.数字图形处理.北京:电子工业出版社,2002:58-65
[8]程佩青.数字信号处理.北京:高等教育出版社,2001:37
[9]李弼程,彭天强,彭波等编著.智能图像处理技术.北京:电子工业出版社,2004:221-305
[10]章毓晋.图像处理和分析基础.北京:高等教育出版社,2002
[11]张庆磊.基于图像处理的大空间火灾探测技术研究:[硕士学位论文].成都:西华大学,2007
[12]章毓晋.图像分割.北京:科学出版社,2001:43-65
[13]吴龙标,方俊,谢启源.火灾探测与信息处理.北京:化学工业出版社,2006:99-160,243-250
[14]安丰凌,孙劲光,张新君.一种火灾报警系统中火焰区域的检测方法.现代计算机,2007.10:44-46
[15]王亚军,徐大芳,陈向成,杨圣.基于火焰图像动态特征的火灾识别算法.测控技术,2007.26(5):7-9
[16]易正俊.多源信息智能融合算法:[博士学位论文].四川:重庆大学,2002
[17]姜长生,王从庆等.智能控制与应用.北京:科学出版社,2007:92-103
[18]葛哲学,孙志强.神经网络理论与MATLAB R2007实现.北京:电子工业出版社,2007:46-55
[19]王志鹏.基于信息融合技术的故障诊断方法的研究及应用:[博士学位论文].大连:大连理工大学,2001:31-33
[20]水祺,吕景辉.火灾报警系统中的信息融合.国外电子测量技术,2005.6:39-41
[21] Derbel F, Hom M, Trankler H R. System Identification techniques for simulating fire detector signals. MTC 99,IEEE Multi-conference ,Venice Italy,1999:898-903
[22] Otsu N.A threshold selection method from gray-level Histogram.IEEE Trans Actions on SMC,1979(9):652-655.
[23] NFPA92B.Smoke Management Systems in Malls, Atria, and Large Large Areas. National Fire Association.USA,1991
[24] S.Nakanishi,et al., Intelligent Fire Warning System Using Fuzzy Technology. Journal of Japan Society for Fuzzy Theory and Systems, 1993.Vol.5(1):95-107
[25] Timon Chin-Ting and Philip M.Wolfe,Amalgamation of neural networks and logic systems-a survey. Computers & Industrial Engineering, 1995.Vol.29(1-4): 193-197
[26] G.O.Hansell, H.P.Morgan, Design Approaches for Smoke Control in Atrium Buildings, Building Research Establishment Report, 1994
[27] T. Tanaka,T.Yamana, Smoke Control in Large Scale Spaces, part 1. Fire Science & Technology,1985.Vol.5(1): 21-40
[28] D. Park, A.Kandel and G.Langholz, Genetic-Based New Fuzzy Reasoning Models with Application to Fuzzy Control,IEEE Transactions on Systems, Man, and Cybernetics, 1994.Vol.24
[29] C.C.Lee, Fuzzy logic in control systems, IEEE trans. Syst. Man Cybern, 1990.Vol.20(2):404-435
[30] Muller H.C, Fisclier A.A Robust Fire Detection Algorithm for Temperature and Optical Smoke Density using Fuzzy Logic, IEEE Congference on security Technology,1995:197-204
[31] Nonlinear neural network, Principles, mechanism, and architectures,Neural Network,1988.Vol.1:17-61
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