Smoke Recognition Algorithm Based on Lightweight Convolutional Neural Network

YUAN Fei; ZHAO Xuyan; WANG Yige; ZHAO Zhisheng

doi:10.3969/j.issn.0258-2724.20190777

Volume 55 Issue 5

Oct. 2020

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Journal of Southwest Jiaotong University > 2020 > 55(5): 1111-1116, 1132.

YUAN Fei, ZHAO Xuyan, WANG Yige, ZHAO Zhisheng. Smoke Recognition Algorithm Based on Lightweight Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 1111-1116, 1132. doi: 10.3969/j.issn.0258-2724.20190777

Citation:

YUAN Fei, ZHAO Xuyan, WANG Yige, ZHAO Zhisheng. Smoke Recognition Algorithm Based on Lightweight Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 1111-1116, 1132. doi: 10.3969/j.issn.0258-2724.20190777

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Smoke Recognition Algorithm Based on Lightweight Convolutional Neural Network

doi: 10.3969/j.issn.0258-2724.20190777

Received Date: 07 Aug 2019
Rev Recd Date: 19 Nov 2019

Available Online: 07 Jul 2020

Publish Date: 01 Oct 2020

Abstract

Abstract

As smoke images are ambiguous, and the background is complex and variable, it is difficult to capture the effective features, resulting in high false positive rates and false negative rates. In addition, the deep convolutional neural network has a complicated structure and many parameters, and it is difficult to control the calculation time within one millisecond, which becomes a major problem for real-time fire warning. In order to deal with these obstacles, a lightweight convolutional neural network SInception (sequeeze-and-excitation inception) is proposed on the basis of four Inception structures, which significantly reduces the number of network parameters and calculation amount. It adds SE Block (sequeeze-and-excitation block) for smoke so that features are redistributed to make them more representative of smoke images. In order to avoid over-fitting due to insufficient training samples, for the data enhancement techniques on the original dataset and generative adversarial network are used to generate more training samples. Subsequently a strategy of integrating the priori features of dark channels is used in experiments. Finally, the network raises the accuracy rate to 99.65%, while for the dataset GAN-Aug-YUAN it reduces the false alarm rate to 0, and the calculation time is only 0.26 ms.
- smoke recognition,
- deep learning,
- computer vision,
- dark channel feature,
- generative adversarial network

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References(15)

References

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