可变视场下的火灾探测算法

张进华; 李婷; 王孙安; 李小虎

您当前的位置：

首页 >

文章列表页 >

可变视场下的火灾探测算法

更新时间：2025-07-09

- 可变视场下的火灾探测算法
- An Early Detection Algorithm for Small Flame Based on Vision System with Variant Field of View
- 西安交通大学学报 2012年46卷第10期页码：29-35
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2012-03-10. 作者简介: 张进华(1979-),男,副教授. 基金项目: 国家自然科学基金资助项目(60905136)
- DOI：
  中图分类号： TP393
- 网络首发：2012-10-10，
  
  纸质出版：2012
- 稿件说明：
移动端阅览
张进华, 李婷, 王孙安, 等. 可变视场下的火灾探测算法[J]. 西安交通大学学报, 2012,46(10):29-35.

An Early Detection Algorithm for Small Flame Based on Vision System with Variant Field of View[J]. 2012, 46(10): 29-35.
张进华, 李婷, 王孙安, 等. 可变视场下的火灾探测算法[J]. 西安交通大学学报, 2012,46(10):29-35. DOI：

An Early Detection Algorithm for Small Flame Based on Vision System with Variant Field of View[J]. 2012, 46(10): 29-35. DOI：

摘要

针对火灾发生初期由火焰面积小、特征不够明显且易受干扰等因素所导致的火焰识别困难的问题

提出一种可变视场下小面积火焰快速探测方法.该方法利用旋转云台实现小面积火焰疑似区域的大监控范围跟踪和视野中央聚焦; 通过镜头变焦处理对小面积火焰进行自动放大

提高火焰区域“多分辨”视觉识别特征的显著性; 采用自适应区域增长算法对火焰图像进行识别

有效完成火焰疑似区域分割; 通过建立的信任度概率模型

将疑似概率作为火焰判定的量化参数

用于火焰区域的最终确定

从而在识别算法和图像获取硬件设备两方面改善火焰探测系统的工作效率.实验结果表明

火焰成像质量得到明显改善

火焰特征识别速度得以明显加快

小火焰检测精度显著提升

可广泛应用于火灾早期以及远距离火焰检测与预警.

Abstract

A fast detection method for small flame area in variable visual fields is proposed to solve the difficulties of flame identification caused by the small flame area

unapparent features and its easy-interferential in fire prime. A Pan-Tilt-Zoom is used to track and center the small suspected fire fields in a large range region. Then

the zoom lens is handled to enlarge the small area

and to enhance the “multi-resolution” visual recognition feature. Then

an adaptive region growing algorithm is adopted to detect and segment the suspected flame region. Finally

a confidence probability model is built and the suspected probability is used as the quantitative parameters to confirm flame recognition. Experimental results show that the flame imaging quality is improved clearly

and that the small fire detection precision is greatly increased. The system can be widely used in early fire finding and long-range flame detection and warning.

关键词

Keywords

references

CELIK T, DEMIREL H. Fire detection in video sequences using a generic color model[J]. Fire Safety Journal, 2009,44(2):147-158.

LU Taifang, PENG Chienyuan, HORNG Wenbing, et al. Flame feature model development and its application to flame detection[C]∥Proceedings of the First International Conference on Innovative Computing,Information and Control. Piscataway,NJ,USA:IEEE,2006: 158-161.

HORNG Wenbing, PENG Jianwen, CHEN Chihyuan. A new image-based real-time flame detection method using color analysis[C]∥Proceedings of the IEEE International Conference on Networking, Sensing and Control. Piscataway,NJ,USA:IEEE,2005:100-105.

PHILLIPS W III, SHAH M, LOBO N V. Flame recognition in video[J].Pattern Recognition Letters,2002,23(1/2/3):319-327.

GREENSPAN H J,GOLDBERGER M A. A probabilistic framework for spatio temporal video representation and indexing [C]∥Proceedings of the 7th European Conference on Computer Vision. Berlin, Germany: Springer, 2002:461-475.

BLEKAS K,LIKAS A,GALATSANOS N P. A spatially constrained mixture model for image segmentation [J]. IEEE Transactions on Neural Networks, 2005, 16(2): 494-498.

LIU Feng, GLEICHER M. Region enhanced scale-invariant saliency detection[C]∥Proceedings of the International Conference on Multimedia and Exposition. Piscataway,NJ,USA:IEEE,2006: 1477-1480.

KO B C, CHEONG K H, NAM J Y. Fire detection based on vision sensor and support vector machines [J].Fire Safety Journal, 2009, 44(3):322-329.

TOREYIN B U, DEDEOGLU Y, CETIN A E. Flame detection in video using hidden Markov models [C]∥Proceedings of the IEEE International Conference on Image Processing. Piscataway, NJ, USA: IEEE, 2005:1230-1233.

袁非牛,廖光煊,张永明,等.计算机视觉火灾探测中的特征提取[J].中国科技大学学报,2006,36(1):39-43.

YUAN Feiniu,LIAO Guangxuan, ZHANG Yongming, et al. Feature extraction for computer vision based fire detection[J].Journal of University of Science and Technology of China,2006,36(1):39-43.

CELIK T. Bayesian change detection based on spatial sampling and Gaussian mixture model [J].Pattern Recognition Letters, 2011, 32(12):1635-1642.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合电解槽阵列协同的风光耦合制氢系统双层多目标优化

三塔合一间接空冷系统结构参数对夏季运行特性的影响研究

EBSILON软件在热电联产系统优化中的应用综述

采用试验设计方法的第三代CO2气体箔轴承箔片几何参数敏感性分析

电子级超纯氮深冷空分流程节能优化研究