摘要
在图像型火灾检测方法中,火灾特征的选取有一定的随机性和复杂性,仅仅依靠低层次的图像特征难以完整地描述复杂背景下的火灾图像。将深度学习技术应用到火灾检测领域,提出基于卷积神经网络的火灾检测方法,搭建包含3层全连接层的网络模型,使用Relu函数作为激活函数;然后基于Tensorflow平台实现该网络结构模型。在公开的火灾数据库上进行实验,结果表明,所提方法的火灾检测效果优于传统的图像型火灾检测算法,避免了由于选取特定火灾特征进行检测识别带来的局限性。
In image-based fire detection methods,the fire feature selection has a certain randomness and complexity,and it is difficult to completely describe the fire images in complex background by only relying on low level image features.Therefore,a fire detection method based on the convolutional neural network is proposed by applying the deep learning technology to the fire detection field.A network model including three full connection layers is built,taking the Relu function as the activation function.The network structure model is implemented based on the Tensorflow platform.An experiment was carried out on the public fire database.The experimental results show that the proposed method has a better fire detection effect in comparison with traditional image-based fire detection algorithms,which can avoid the limitations brought by detection recognition using selection of specific fire characteristics.
作者
杨柳
张德
王亚慧
YANG Liu;ZHANG De;WANG Yahui(School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《现代电子技术》
北大核心
2019年第10期141-145,共5页
Modern Electronics Technique
基金
国家自然科学基金资助项目(61473027)
北京建筑大学市属高校基本科研业务费专项资金资助(X18068)~~
关键词
火灾检测
卷积神经网络
图像处理
城市火灾
模式识别
深度学习
fire detection
convolutional neural network
image processing
city fire
pattern recognition
deep learning
作者简介
杨柳(1992—),男,重庆人,硕士研究生,主要研究方向为图像处理;张德(1979-),男,河北冀州人,博士,讲师,主要研究方向为模式识别和图像分类;王亚慧(1962-),男,内蒙古乌兰察布人,博士研究生,教授,主要研究方向为自动控制理论和复杂系统建模。
