基于运动筛选和3D卷积的视频早期烟雾检测  

Video Early Smoke Detection Based on Motion Extraction and 3D Convolutional Neural Network

作　　者：高联欣 魏维[1] 胡泳植 冯宇浩 GAO Lianxin;WEI Wei;HU Yongzhi;FENG Yuhao(College of Computer Sciences,Chengdu University of Information Technology,Chengdu 610225,China)

机构地区：[1]成都信息工程大学计算机学院,成都610225

出　　处：《计算机工程与应用》2020年第17期266-272,共7页Computer Engineering and Applications

摘　　要：针对基于视频的早期烟雾检测在复杂环境下的高误报和高漏检问题,提出了一种基于运动筛选疑似区域的方法并设计了一个输入为6帧图片的多尺度3D卷积神经网络(6M3DC)来进行视频烟雾检测的算法。将视频帧进行均值滤波后通过背景差分模型获取运动区域并计算获得区域所在块,通过颜色判断和均值HASH算法对运动块进行筛选提取疑似块并将不符合条件的块更新到背景图。通过组合连续6帧相同区域的疑似块输入多尺度3D卷积神经网络进行检测,将检测为烟雾的块标记,非烟雾块更新到背景图。实验结果表明,算法对缓慢运动的烟雾有一定的适应性,可以较好地在复杂环境下检测出烟雾。To solve the problem of high false alarm and high missed detection in the complex environment of early smoke detection based on video,a method based on motion extraction of suspected areas is proposed and a multi-scale 3D convolutional neural network with input of 6 frames(6M3DC)is designed for video smoke detection.Firstly,the motion regions are obtained through the background difference model after average filtering and the positions of the block in which the motion regions are located are calculated,and then the motion blocks are extracted by color judgment and mean HASH algorithm and the nonconforming blocks are updated to the background image.Finally,by combining the suspected blocks of the same region of 6 consecutive frames as the input for the 3D convolutional neural network for detection,blocks detected as smoke are marked and non-smoke blocks are updated to the background image.The experimental results show that the algorithm is adaptive to slow moving smoke and can detect smoke in complex environment.

关 键 词：早期烟雾 颜色判断 均值HASH 多尺度3D卷积 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]