Open AccessFire recognition with convolutional neural network based on transfer learning
Author(s) -
Xiang Liu,
Guangcun Wei,
Wansheng Rong,
Xinguang Xiao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012139
Subject(s) - transfer of learning , computer science , convolutional neural network , artificial intelligence , deep learning , feature (linguistics) , machine learning , pattern recognition (psychology) , constant false alarm rate , artificial neural network , transfer (computing) , feature extraction , layer (electronics) , philosophy , linguistics , chemistry , organic chemistry , parallel computing
The fire recognition model based on deep learning can avoid many defects in the traditional method, but its construction requires a large amount of data to train the network parameters, and it takes a lot of time. In order to improve the accuracy of the model, this paper proposes a fire recognition model TNVGG-19 (Transfer learning + Newly fully connected layer module + VGG-19) with convolutional neural network based on transfer learning. First, we use the strategy of transfer learning to train the feature extraction network. Secondly, based on the VGG-19 model, this paper adds a newly designed fully connected layer module. Considering that flame data belongs to small sample data, we adopted a data augmentation strategy. Experiments show that the TNVGG-19 fire recognition model based on transfer learning proposed in this paper can effectively improve the accuracy of fire prediction and reduce the false alarm rate.