Abstract
Image smoke detection is a challenging task due to the difference of color, texture, and shape of smoke. In recent years, deep learning has greatly improved the performance of image classification and detection. In this paper, we propose a Dual-Channel Convolutional Neural Network (DC-CNN) using transfer learning for detecting smoke images. Specifically, an AlexNet network with transfer learning, used to extract generalized features, is designed on the first channel as the main framework of entire network. The second channel is a tidy convolution neural network for extracting specific and detailed features. To guarantee the robustness of the network, two channels of the network are trained separately and their features are fused in the concat layer. The experimental data sets consist of smoke images and non-smoke images, and some challenging non-smoke images are added into the data sets as a supplement. Experimental results show that the proposed method can work effectively and achieve detection rate above 99.33%.
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Acknowledgments
This work was supported in part by Plan Program of Tianjin Educational Science and Research (Grant no.2017KJ087), Tianjin Science and Technology Major Projects and Engineering (grant No.17ZXHLSY00040 and No.17ZXSCSY00090).
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Zhang, F., Qin, W., Liu, Y. et al. A Dual-Channel convolution neural network for image smoke detection. Multimed Tools Appl 79, 34587–34603 (2020). https://doi.org/10.1007/s11042-019-08551-8
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DOI: https://doi.org/10.1007/s11042-019-08551-8