CAJ | 학술논문

为提高基于视频图像的公路隧道火灾火焰识别率，在对火焰动态特征研究成果之上，利用BP神经网络融合火焰静态特征，对公路隧道视频火焰进行综合识别．火焰动态特征选取作者研究的火焰边缘运动量（AM FE）和火焰区域跳动特征，火焰静态特征选取前人研究的尖角数目、火焰颜色特征和圆形度．将此5种火焰特征作为BP神经网络的输入，达到融合火焰多特征信息并实现火焰综合识别的目的．实验结果表明，火焰识别率稳定在86．2％～96．5％之间，验证了该方法的可靠性．
위제고기우시빈도상적공로수도화재화염식별솔，재대화염동태특정연구성과지상，이용BP신경망락융합화염정태특정，대공로수도시빈화염진행종합식별．화염동태특정선취작자연구적화염변연운동량（AM FE）화화염구역도동특정，화염정태특정선취전인연구적첨각수목、화염안색특정화원형도．장차5충화염특정작위BP신경망락적수입，체도융합화염다특정신식병실현화염종합식별적목적．실험결과표명，화염식별솔은정재86．2％～96．5％지간，험증료해방법적가고성．
In order to improve the flame identification rate for highway tunnel based on video ,BP neu‐ral network was employed for integration of static features to identify flame on the basis of dynamic fea‐tures ,such as the amount of movement of flame edge(AMFE) and flame area beating feature ,from our ow n previous studies .Flame static features such as flame circular degree ,flame color feature ,and flame circular degree were adopted from the previous studies of others .And the five features were used as the in‐put of BP neural network to identify flame .The experiment results showed that the rage of flame identifica‐tion rate was 86 .2% —96 .5% ,which verified the reliability of the method .