Image-based fire detection can effectively solve the problems of large space fire detection contactlessly and rapidly. It is a new research direction in fire detection. Its essential issue is the classification of flames and disruptors. The ordinary detection methods are to extract one or a few characteristics of the flame in the image as a basis for identification. The disadvantages are to need a large number of experiential thresholds and the lower recognition rate by the inappropriate feature selection. Considering the entire characteristics of fire flame, a flame detection method based on Independent Component Analysis {ICA) and Support Vector Machine (SVM) was proposed. Firstly, a series of frames were pre-processed in RGB space. And suspected target areas were extracted depending on the flickering feature and fuzzy clustering analysis. Then the flame image features were described with ICA. Finally, SVM model was used in order to achieve flame recognition. The experimental result shows that the proposed method improves the accuracy and speed of image fire detection in a variety of fire detection environments.%图像型火灾探测具有非接触性、反应快等优点,可有效解决大空间火灾探测难题,是火灾探测新的研究方向,其核心问题是火焰和干扰物的分类识别.常用方法是提取火焰在图像上表现的单个或某几个特征信息作为识别依据,需要设置大量经验阚值,识别率常因特征选择不合适而受到影响.通过对火焰整体特性的研究,提出了基于独立成分分析和支持向量机的火焰探测方法.首先在RGB空间建立颜色模型对连续数帧火灾图像预处理,并进行频闪特性和模糊聚类分析提取疑似目标区域,根据独立成分分析线性变换一对一和可逆性估计出基函数描述火焰图像特征,最后用支持向量机模型实现火灾探测.实验结果表明,该方法提高了图像型火灾探测精度和速度,可适用于多种火灾探测场景.
展开▼
