基于图像处理技术的烟雾检测在家庭火灾防范中应用

Application of Smoke Detection Based on Image Processing Technology in Home Fire Prevention

针对家庭火灾防范中缺少更有效、快速的实时烟雾检测问题,本文设计了一种基于图像处理技术的烟雾检测算法,能够对家庭住宅内部进行实时烟雾检测,从而实现火灾防范。本算法主要使用单阶段的端到端YOLO v3算法,对输入的烟雾数据进行学习,提取网络中不同卷积层的特征图进行联合训练。实验结果表明,和双阶段网络的烟雾图像检测方法相比,该方法检测速度快,实时效果好,同时也取得了较高的检测准确率,具有较高的实际应用价值。

In view of the lack of more effective and fast real-time smoke detection in home fire prevention,this paper designs a smoke detection algorithm based on image processing technology,which can detect real-time smoke inside a family house,thereby realizing fire prevention.This algorithm mainly uses a single-stage end-to-end YOLO v3 algorithm to learn the input smoke data and extract the feature maps of different convolutional layers in the network for joint training.The experimental results show that compared with the smoke image detection method of the two-stage network,this method has fast detection speed and good realtime effect.At the same time,it has achieved a higher detection accuracy rate and has higher practical application value.