基于最优质量传输光流法和神经网络的火焰和烟雾检测

Fire and Smoke Detection Based on the Optimal Mass Transmission Optical Flow Method and Neural Network

针对传统光流法不适用于气体和液体等图像检测的问题,提出了使用最优质量传输光流作为复杂过程的低维描述符用于火焰和烟雾检测的方法。检测过程可以抽象成一种关于时空像素邻域的监督式贝叶斯分类问题,其特征矢量是由最优质量传输光流速度和R、G、B颜色通道构成的,并采用单隐层神经网络分类器进行特征提取,最后通过分析像素概率来判断属于火焰或者是烟雾。实验结果表明,该方法成功的区分了烟雾和颜色相似的白云,同样区分了火焰和与火焰颜色相似的背景,具有较强的鲁棒性。

Aiming at the problems that the traditional optical flow method is not suitable for gas and liquidimage detection, this paper proposes a method which uses the optimal mass transmission optical flow as a lowdimensional descriptor of the complex process for fire and smoke detection. The detection process can be abstractedinto a problem about the supervised Bayesian classification of spatio-temporal neighborhood pixels; feature vectorsare composed of the optimal mass transmission optical flow and R, G, B color channels and the single hidden layerneural network classifier are employed. Finally, we determine the pixel belongs to the flame or belongs to the smokeby the analysis the pixel probability. Experiments show that the proposed method successfully distinguishes smokeand the color-similar cloud, also distinguish between the flame and the flame color-similar background, and hasstrong robustness.