基于光流法和深度学习的燃气火焰稳定性

Gas-Fired Flame Stability Based on Optical Flow Method and Deep Learning

结合光流法和深度学习对燃气火焰稳定性进行了研究.采用光流法直接计算出火焰图像的光流矢量,观察火焰在二维图像中的脉动情况,并提出光流脉动评价模型,可以评估火焰的燃烧稳定性.此外,搭建基于VGG-Nets的深度卷积神经网络模型,在ImageNet预训练权重上进行微调,结合火焰静态与动态特征,实现了对五种典型燃烧状态的分类与识别.结果表明:该方法对火焰的不同燃烧状态具有很好的判断能力,对不稳定燃烧的火焰识别率很高.

The stability of gas-fired flame is studied by combining the optical flow method and deep learning.The optical flow vector of the flame image is directly calculated by using the optical flow method.The pulsation of the flame in the two-dimensional image is observed,and an optical flow pulsation evaluation model is proposed to evaluate the stability of the flame.In addition,a deep convolutional neural network based on VGG-Nets is built and fine adjustments are made on ImageNet pre-training weights.Combining the static and dynamic characteristics of flames,the classification and recognition of five typical combustion states are achieved.The results show that this method has a good judgment ability for different combustion states of flames and a high recognition rate for unstable combustion flames.