深度学习的MPCANet火灾图像识别模型设计  

Design of MPCANet fire image recognition model for deep learning

作　　者：张秀玲[1,2] 侯代标 张逞逞 周凯旋 魏其珺 Zhang Xiuling;Hou Daibiao;Zhang Chengcheng;Zhou Kaixuan;Wei Qijun(Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao 066004,China;National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University,Qinhuangdao 066004,China)

机构地区：[1]燕山大学河北省工业计算机控制工程重点实验室,河北秦皇岛066004 [2]燕山大学国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004

出　　处：《红外与激光工程》2018年第2期40-45,共6页Infrared and Laser Engineering

基　　金：河北省自然科学基金-钢铁联合研究基金(E2015203354);河北省高校创新团队领军人才培育计划项目(LJRC013);河北省教育厅科学研究计划河北省高等学校自然科学研究重点项目(ZD2016100);2016年燕山大学基础研究专项课题(理工类)培育课题(16LGY015);秦皇岛市科技局自筹项目(201703A229)

摘　　要：针对火灾发生时,火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题,提出了张量对象特征提取的多线性主成分分析(Multilinear Principal Component Analysis,MPCA)深度学习算法的火灾图像识别新方法。利用MPCANet建立火灾图像识别模型,通过MPCA算法学习滤波器作为深度学习网络卷积层卷积核,对张量对象的高维图像进行特征提取,并把蜡烛图像和烟花图像作为干扰。通过仿真实验并与其他火灾图像识别方法对比得到提出的图像识别方法识别精度达到了97.5%、误报率1.5%、漏报率1%。实验表明:该方法可以有效解决火灾图像识别存在的问题。In view of the complicated background of the fire image,the complicated process of extracting the artificial feature,the poor generalization ability of the fire image,the low accuracy,false alarm rate,missing rate,the novel method for detecting fire images of multilinear principal component analysis(MPCA)was presented in the paper.The fire image recognition model was established by using MPCANet.Through the MPCA algorithm,the learning filter was used as the convolution kernel of deep learning network convolution layer,and the feature extraction of high dimensional images of tensor objects was taken,and candle images and fireworks images were taken as interference.Compared with other fire image recognition methods,the recognition accuracy of the proposed image recognition method reaches 97.5%,false alarm rate of 1.5%,missing rate of 1%.Experiments results show that this method could effectively solve the problems of fire image recognition.

关 键 词：深度学习 MPCANet 张量对象分析 火灾图像识别 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]