[1]何世钊,杨宣访,陈晓娟.支持向量机与BP网络在火灾图像探测上的比较[J].智能系统学报,2011,6(4):339-343.
HE Shizhao,YANG Xuanfang,CHEN Xiaojuan.Comparisons between a support vector machine and BP neural network for video image fire detection[J].CAAI Transactions on Intelligent Systems,2011,6(4):339-343.
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HE Shizhao,YANG Xuanfang,CHEN Xiaojuan.Comparisons between a support vector machine and BP neural network for video image fire detection[J].CAAI Transactions on Intelligent Systems,2011,6(4):339-343.
支持向量机与BP网络在火灾图像探测上的比较
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
6
期数:
2011年第4期
页码:
339-343
栏目:
学术论文—机器感知与模式识别
出版日期:
2011-08-25
- Title:
- Comparisons between a support vector machine and BP neural network for video image fire detection
- 文章编号:
- 1673-4785(2011)04-0339-05
- Keywords:
- fire detection; shape features; SVM; BP neural network
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 针对BP神经网络和支持向量机在火灾探测上存在的理论差别，分别构建了基于此2种方法的火灾图像探测方法.2种方法均依据火焰颜色分布规律实现了目标区域的分离，并将目标区域的形状特征及变化值作为判据.通过对火灾实验样本的训练及识别，2种方法的探测表现得到了比较与分析.实验结果表明基于支持向量机的火灾探测方法具有快速收敛特性及所需较少训练样本的优点.同时，BP神经网络对测试集较少的错判反映出其良好的非线性映射能力，适合求解内部机制复杂的问题.
- Abstract:
- According to the theoretical differences between a back propagation (BP) network and support vector machine (SVM) in relation to fire detection, two kinds of video image fire detection methods based on a BP network and SVM, respectively, were constructed. Judging from color distribution of the flames, the objective regions were separated in both methods, and their shape features along with the changes in shape features were extracted as criteria. The performance of each method was compared and analyzed after conducting many experiments. The experimental results show that the SVM had a high convergence rate and needed fewer training samples. At the same time, fewer misjudgments of testing samples confirmed that the BP network was more suitable for solving complex internal mechanism problems due to its good mapping capability.
- 参考文献/References:
-
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备注/Memo
收稿日期： 2010-06-19.
基金项目：国家自然科学基金资助项目（50721063）.
通信作者：何世钊.E-mail:heshizhao_chn@126.com.
作者简介：
?何世钊, 男, 1986年生,硕士研究生，主要研究方向为检测技术与自动化装置.
杨宣访，男, 1968年生, 副教授, 硕士生导师.主要研究方向为自动测试、电路故障诊断、电力系统诊断.先后主持、参与军队、海军等多项重点科研和工程项目.获得军队科技进步一等奖1项,二等奖2项,三等奖4项.发表学术论文20余篇,参与编写教材1部.
陈晓娟, 女, 1981年生, 博士研究生, 主要研究方向为数字信号处理.
更新日期/Last Update:
2011-09-30