一种新型的图像型火灾识别算法的研究

Research for a new fire image detection technology

图像型火灾探测技术是一种新型的探测技术,可以有效地克服传统火灾探测技术的缺陷.针对背景复杂的火灾图像,首先利用差分技术、RGB颜色分割技术和形态特征分割技术建立3层复合分割模型,排除大部分干扰,得到火焰疑似区域;然后分析火焰疑似区域的相似性测度、面积变化值、致密度、偏心率和质心点偏移距离等特征,这些特征可以较全面地表征火灾信息;最后利用RBF神经网络建立火灾识别模型,将提取出的火焰特征作为输入量,对火灾图像进行分类识别.仿真结果表明,该算法对不同场景的火灾识别具有较高的准确率.

The fire detection technology based on image processing is a new and effective detection method, which can effectively overcome the disadvantages of traditional methods. To solve the problems that the fire image are dif- ficult to be segmented and the background is complex, fire image need be enhanced by using some processing tech- nologies. The three composite segmentation model is established by using threshold segmentation, RGB space mod- el, morphology characters segmentation in order to exclude most of the disturbance and get the suspicious flame zone. And then, some characteristics such as similarity measure, area, degree of roundness, center of excursion dis- tance, and circularity of the fire image, are studied and these features can efficiently characterize the fire information. Finally, the model of fire detector algorithms is established by using RBF neural network in which flame characters are taken as inputs and used to classify and recognize fire image. The experiment results show that the algorithm has higher reorganization rate in different environments.