基于总有界变分的森林火灾烟雾图像检测方法

Smoke image detection method of the forest fire based on total bounded variation

森林火灾烟雾浓度升高时,所对应的图像模糊程度升高,总有界变分会逐渐下降,基于变分的特征性质,可以将边界之间的差异有效表征出来。由此,提出一种基于总有界变分的森林火灾烟雾图像检测方法。以分块平稳分析的思想对目标函数求极值,得到总有界值,通过两次比较总有界变分值从分块结果图中提取疑似烟雾分块,利用特征数据的融合聚类处理获得最终的疑似烟雾区域。为了得到更好的烟雾检测效果,对疑似烟雾特征区域进行运动特性分析,融合判定烟雾区域,给出火灾报警。算法屏蔽了对烟雾静态特征的复杂计算,在对疑似烟雾特性进行分析时,只需关注其运动特征便可以准确进行烟雾检测输出,避免了繁琐计算带来的误差,对比验证效果显示,算法结果输出高效稳定。

When the smoke concentration of forest fire increases,the blurring degree of the corresponding image increases,and the total bounded variation gradual declines.Based on the characteristics of the variation,the difference between the boundaries can be effectively represented.Therefore,a detection method of forest fire smoke image is proposed based on total bounded variations.The objective function is extremum calculated with the idea of block stationary analysis,and the total bounded value is obtained.By comparing the total bounded variational value twice,the suspected smoke is extracted from the block result graph,and the fused clustering processing of feature data is used to obtain the final suspected smoke area.In order to get better smoke detection effect,it analyzes the motion characteristic of suspected smoke feature area,and the smoke area is judged by fusion,then the fire alarm is given.The algorithm shields the complex calculation of the static characteristics of the smoke.When the suspected characteristics of smoke are analyzed,the smoke can be accurately detected by only its motion characteristics,which avoids the errors caused by cumbersome calculation.The comparison and verification results show that the output of the algorithm is efficient and stable.