基于深度学习的VGG16 图像型火灾探测方法研究

为了快速、有效地检测不同场景下的火灾信息，基于深度迁移学习设计了一种改进VGG16 的图像型火灾检测方法。搜集不同场景下的照片，使用离线数据增强技术增加样本数量，对VGG16 进行改进，并使用迁移学习的方法训练火灾识别模型。结果表明：改进的VGG16 网络对于火灾现场的图片分类识别准确率为98.7%,优于Resnet50 网络和Densenet121 网络，可快速、准确地检测到火灾信息。

Research on fire detection of improved VGG16 image recognition based on deep learning

In order to quickly and effectively detect fire in different scenes and avoid missing the best time for fire fighting,an improved VGG16 image recognition fire detection method is designed based on deep transfer learning.Collect photos of fire and no fire in different scenarios,use offline data enhancement methods to increase the number of samples,improve VGG16,and use transfer learning methods to train fire recognition models.The experimental results show that the improved VGG16 model has a 98.7%accuracy in classification and recognition of pictures with and without fire,which is better than the Resnet50 model and the Densenet121 model.It is proved that the method has high accuracy in identifying the situation of flames after the fire,and can detect the fire quickly and accurately.