Smoke recognition method based on dense convolutional neural network
CHENG Guangtao1, GONG Jiachang2, LI Jian1
1.Department of Research and Development, National Center for Fire Engineering Technology, Tianjin 300381, China
2.Department of Audio-Visual Information Detection Technology, Criminal Investigation Police University of China,ShenyangLiaoning 110854, China
To address the poor robustness of the extracted image features in traditional smoke detection methods, a smoke recognition method based on Dense convolution neural Network (DenseNet) was proposed. Firstly, the dense network blocks were constructed by applying convolution operation and feature map fusion, and the dense connection mechanism was designed between the convolution layers, so as to promote the information circulation and feature reuse in the dense network block structure. Secondly, the DenseNet was designed by stacking the designed dense network blocks for smoke recognition, saving the computing resources and enhancing the expression ability of smoke image features. Finally, aiming at the problem of small smoke image data size, data augmentation technology was adopted to further improve the recognition ability of the training model. Experiments were carried out on public smoke datasets. The experimental results illustrate that the proposed method achieves high accuracy of 96.20% and 96.81% on two test sets respectively with only 0.44 MB model size.
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