摘要
图像型火灾探测的主要问题是关于火焰和干扰物的识别。通过提取火灾图像特征,利用支持向量机来进行识别。为提高火灾准确预报率,用参数优化后的支持向量机来预报。提出一种混沌粒子群算法对支持向量机进行参数优化。实验表明,改进的粒子群算法比传统方法的火灾准确预报率有大幅提高,可以进一步降低火灾探测系统的误报。
The main problem of image fire detection is the recognition for fire flame and interferences. Main characteristics of fire flame were obtained, and use support vector machine technology for image recognition. To improve the prediction accuracy rate of fire, the parameters optimized support vector machine to predict. This paper presents a chaotic particle swarm optimization algorithm for support vector machine parameter optimization. The experiment results show that the improved particle swarm optimization method in the prediction accuracy rate of fire has a substantial increase than that of the tradition, and can further reduce the false alarm of fire detection systems.
出处
《信息技术》
2012年第6期19-21,共3页
Information Technology
基金
湖北省教育厅科研项目(D20091103)
关键词
火灾图像
支持向量机
参数优化
混沌粒子群
fire images
support vector machine
parameter optimization
chaotic particle swarm optimization
