摘要
对火焰动态特征难以统一描述的问题,提出一种无量纲的检测方法。用"搜寻"的方法分割出火焰的可疑区域,分析火焰初期的特性,提取出三个无量纲特征因子并在贝叶斯分类器中训练,最后实现对火灾火焰的检测。其中"火焰动态常数"因子具有"稳定"的特性,其统计取值区间为[-0.003,0.003],突破了传统研究的时空局限性,不受火焰发展阶段、空间探测尺度以及监控设备种类的影响。对于实验中的不同远近的200帧序列的火焰检测,无量纲特征识别同一般特征识别结果相比较,正确识别率均超过90%。实验结果表明,无量纲动态特征因子能更好地描述火焰的特征,提高火焰识别的效率,增强火检系统的鲁棒性和可靠性。
Concerning the difficulty in consistent description of flame dynamic characteristics,a dimensionless detection method was proposed.A ＂search＂ method was made to segment the suspicious districts.Then three dynamic dimensionless characteristic factors were extracted to train a Bayesian classifier after analyzing the characteristics of flame and the final detection result was realized.Of all three characteristic factors,the ＂flame dynamic constant＂ factor has ＂stable＂ characteristics and its statistical values range from-0.003 to 0.003.It has broken the spatio-temporal restriction of traditional research and is free from the influence of the flame development stages,space-scales of detection and varieties of surveillance devices.Under the flame detection experiments on different distance sequences of 200 frames,the correct recognition rate based on dimensionless features,compared with the general features,was almost over 90%.The experimental results show that the proposed factors are better able to describe the flame characteristics and they can greatly improve the efficiency of flame detection and boost the robustness of fire detection system.
出处
《计算机应用》
CSCD
北大核心
2012年第7期1894-1898,共5页
journal of Computer Applications
关键词
火焰特征
火焰检测
图像分割
无量纲
flame characteristic
flame detection
image segmentation
dimensionless
