基于卷积神经网络的火灾视频图像检测

Fire video image detection based on convolutional neural network

随着计算机技术的发展,融合计算机视觉、机器学习、深度学习等技术的火灾图像处理技术得到了广泛的研究和应用。针对传统图像处理方法预处理过程复杂且误报率高等问题,提出一种基于深度卷积神经网络模型进行火灾检测的方法,其减少了复杂的预处理环节,将整个火灾识别过程整合成一个单深度神经网络,便于训练与优化。针对识别过程中类似火灾场景对火灾检测产生干扰的问题,利用火灾的运动特性,创新性地提出利用火灾视频前后帧火灾坐标位置变化来排除灯光等类似火灾场景对检测的干扰。对比了众多深度学习开源框架后,选择Caffe框架进行训练及测试,实验结果表明,该方法实现了对火灾图像的识别和定位,适应于不同的火灾场景,具有很好的泛化能力和抗干扰能力。

With the development of computer technology,fire image processing technology combining computer vision,machine learning,deep learning and other technologies has been widely studied and applied.Aiming at the complex preprocessing process and high false positive rate of traditional image processing methods,this paper proposes a method based on deep convolutional neu-ral network model for fire detection,which reduces complex preprocessing links and integrates the whole fire identification process into one single depth neural network for easy training and optimization.In view of the problem of fire detection caused by similar fire scenes in the identification process,this paper uses the motion characteristics of fire to innovatively propose the combination of fire frame position changes before and after the fire video to eliminate the interference of lights and other similar fire scenes.After comparing many open learning open source frameworks,this paper chooses Caffe framework for training and testing.The experimen-tal results show that the method realizes the recognition and localization of fire images.This method is suitable for different fire scenarios and has good generalization ability and anti-interference ability.