摘要
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。
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.
作者
蒋珍存
温晓静
董正心
孙亦劼
蒋文萍
JIANG Zhen-cun;WEN Xiao-jing;DONG Zheng-xin;SUN Yi-jie;JIANG Wen-ping(School of Elcctrical and Electronic Engincering,Shanghai Institutc of Technology,Shanghai 201418,China;Shanghai Jiaotong University,Shanghai 200240,China)
出处
《消防科学与技术》
CAS
北大核心
2021年第3期375-377,共3页
Fire Science and Technology
基金
国家自然科学基金项目(61703279)。
关键词
消防
火灾检测
图像分类
VGG16
深度学习
fire protection
fire detection
image classification
VGG16
deep learning
