早期油料火灾图像检测及识别技术研究

doi:10.3969/j.issn.1007130X.2010.

J4 ›› 2010, Vol. 32 ›› Issue (2): 72-74.doi: 10.3969/j.issn.1007130X.2010.

早期油料火灾图像检测及识别技术研究

（解放军后勤工程学院军事供油工程系，重庆 400016）

收稿日期:2008-10-05 修回日期:2009-01-05 出版日期:2010-01-25 发布日期:2010-01-26
通讯作者: 陈俊 E-mail:chenjunwoo@126.com
作者简介:陈俊（1981），男，四川泸州人，博士生，研究方向为安全技术与图像处理;杜扬，教授，博士生导师。

Research on the Detection and Identification Technology of Early Oil Fire Images

（Department of Petroleum Supply Engineering,PLA Logistical Engineering University，Chongqing 400016）

Received:2008-10-05 Revised:2009-01-05 Online:2010-01-25 Published:2010-01-26

摘要/Abstract

摘要：

本文提出了一种早期油料火灾图像检测及识别算法。将火焰颜色、亮度及运动特征作为火灾检测与识别的判据，在火焰颜色模型和运动图像差分模型的基础上提出利用离散分形布朗随机增量场模型对早期油料火灾图像进行进一步的判定。模拟坑道实验结果表明，该算法能够有效提高油料火灾检测与识别的准确率，降低误报、漏报率。

关键词: 油料火灾图像, 火焰模型, 差分模型, 离散分形布朗随机增量场模型

Abstract:

An algorithm of early oil fire image detection and recognition is put forward. The flame color, brightness and movement characteristics are chosen as the criteria. The early oil fire images are further detected and recognized by the algorithm of the Discrete Fractal Brownian Incremental Random Field model based on an analysis of the flame model and the differential model. The results of the simulated tunnel experiments show that the algorithm can successfully detect and recognize oil fire.

Key words: oil fire image;flame model;differential model;discrete fractal brownian incremental random field model (DFBIR)

中图分类号:

陈俊, 杜扬, 王冬, 吕航. 早期油料火灾图像检测及识别技术研究[J]. J4, 2010, 32(2): 72-74.

CHEN Dun, DU Yang, WANG Dong, LV Hang. Research on the Detection and Identification Technology of Early Oil Fire Images[J]. J4, 2010, 32(2): 72-74.

早期油料火灾图像检测及识别技术研究

Research on the Detection and Identification Technology of Early Oil Fire Images

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