[1]段锁林,顾川林.基于BP神经网络视频火灾火焰检测方法[J].常州大学学报(自然科学版),2017,(02):65-70.[doi:10.3969/j.issn.2095-0411.2017.02.012]
　DUAN Suolin,GU Chuanlin.Rsearch on the Detection Method Based on the Optimized BP Neural Network for the Visual Fire Flame Recognition[J].Journal of Changzhou University(Natural Science Edition),2017,(02):65-70.[doi:10.3969/j.issn.2095-0411.2017.02.012]
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基于BP神经网络视频火灾火焰检测方法()
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常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
期数:
2017年02期
页码:
65-70
栏目:
计算机与信息工程
出版日期:
2017-03-28

文章信息/Info

Title:
Rsearch on the Detection Method Based on the Optimized BP Neural Network for the Visual Fire Flame Recognition
作者:
段锁林; 顾川林
常州大学 机器人研究所,江苏 常州 213164
Author(s):
DUAN Suolin;  GU Chuanlin
Robotics Institute,Changzhou University,Changzhou 213164,China
关键词:
蚁群算法;  BP神经网络;  混合高斯模型;  多特征融合
Keywords:
ant colony algorithm;  BP neural network;  gaussian mixture model;  multi feature fusion
分类号:
TP 391.4
DOI:
10.3969/j.issn.2095-0411.2017.02.012
文献标志码:
A
摘要:
针对视频火焰图像识别问题,采取一种结合蚁群算法(Ant Colony Algorithm)优化的BP神经网络火灾火焰检测方法。该方法克服了传统神经网络容易陷入局部最优值和收敛速度慢的问题。使用混合高斯模型建立统计模型分割火焰图像。火焰的判别特征采用面积增加率、圆形度和火焰尖角数,并且各特征量作为神经网络的输入量来得到判别火焰的最终概率。通过对大量实验数据的分析,表明该算法在可接受的时间范围内能有效改善火焰识别的准确度。
Abstract:
For the visible flame detection technology, a BP neural network method optimized by the ant colony algorithm is adopted to detect fire in this paper. This method overcomes the disadvantage of falling into local minimum and slow convergence caused by neural network. The gaussian mixture model was used to build statistical model and to divide the fire image. The growth rate of area, roundness and flame angle numbers were adopted as the feature value of flame recognition. In addition, these values will also be the input quantity for the BP neural network. The analysis of experimental data indecates that the algorithm can effectively improve the flame recognition accuracy within acceptable time.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-08-20。
基金项目:江苏省科技支撑计划项目(社会发展)(BEK2013671)。
作者简介:段锁林(1956—),男,陕西岐山人,博士,教授,主要从事智能机器人视觉控制技术研究。
更新日期/Last Update: 2017-04-01