多红外火焰探测中基于决策树的火灾识别

Fire Recognition of Multi-infrared Flame Detection Based on Decision Tree

在多红外火焰探测系统中,提出了一种基于决策树的火灾识别算法。按照特种火灾探测器的国家标准实验的要求,获取实验数据。该算法首先对五个红外火焰探测器获得的数据进行多窗口重叠交叉预处理,然后提取六个火灾特征作为决策树的分类属性,对决策树进行训练、剪枝,最后得到火灾识别的最优决策树模型。将该识别模型应用于在线火灾识别,实验结果表明该决策树分类算法的准确率可以达到95.2%,识别速度在2s以内,较其他的分类识别算法有更高的准确率和更快的识别速度,具有很好的实用性。

In the multi-infrared flame detection, a fire recognition algorithm based on decision tree is proposed. According to the National Standard for Special Fire Detectors,large number of experimental data are acquired. Firstly, the acquired data of the five infrared flame de- tector arc prctreated by the overlapping cross way in the algorithm. Then six characteristics of fire arc extracted as a decision tree classified attributes, and decision tree is trained and pruned. Finally, the optimal decision tree model for fire detection is obtained. This recognition model is applied to the online fire detection,the experimental results show that the accuracy of the decision tree classified algorithm can a- chieve 95.2% and the recognition speed is less than 2s. Compared with other recognizable algorithms,decision tree has higher accuracy and faster recognition speed. It is of great practicality.