基于可见光视觉特征融合的矿井外因火灾监测方法  

Mine external fire monitoring method using the fusion of visible visual features

作　　者：范伟强 李晓宇 刘毅[2] 翁智[1] Fan Weiqiang;Li Xiaoyu;Liu Yi;Weng Zhi(School of Electronic Information Engineering,Inner Mongolia University,Hohhot Inner Mongolia 010021,China;School of Mechanical Electronic and Information Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China)

机构地区：[1]内蒙古大学电子信息工程学院,内蒙古呼和浩特010021 [2]中国矿业大学(北京)机电与信息工程学院,北京100083

出　　处：《矿业科学学报》2023年第4期529-537,共9页Journal of Mining Science and Technology

基　　金：内蒙古自治区高等学校科学研究(NJZY23056);国家重点研发计划(2016YFC0801800)。

摘　　要：为了解决矿井外因火灾监测实时性差、误报率和漏报率高的问题,提出了基于可见光视觉特征融合的矿井外因火灾监测方法。首先,分析了不同监测环境中火源视频图像对应的视觉特征,并设计了火源纹理、尖角、相似系数和闪动频率的提取方法;其次,采用改进的种子区域生长算法对火灾疑似区域进行分割,并利用不同的火源特征提取方法计算火灾疑似区域的动、静态特征;然后,融合所提取的动、静态特征,构建火灾特征向量;最后,构建了基于BP神经网络的火灾监测模型,并对监测模型进行了验证。结果表明,本文提出的火灾监测方法可有效检测不同场景和不同距离下的外因火灾,正确率和检测率分别达到98.60%和99.09%,误检率低至2.00%,并有很强的抗干扰能力和鲁棒性。In order to overcome the problems of poor real-time performance,high false alarm rate and underreport alarm rate of mine external fire monitoring,a method of fire monitoring using the fusion of visible visual features is proposed.Firstly,the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed,and the extraction methods of fire source texture,sharp corners,similarity coefficient and flicker frequency are designed.Then,an improved seed region growth algorithm is used to segment the suspected fire area,and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly,the extracted dynamic and static features are used to construct fire feature vectors.Finally,a fire monitoring model using BP neural network is constructed,and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60%and 99.06%,respectively,the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.

关 键 词：外因火灾 火灾监测 静态特征 动态特征 特征融合 BP神经网络 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]