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An efficient deep neural network with color-weighted loss for fire detection

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Abstract

Fire is one of the most harmful hazards that affect daily life. Compared with sensor-based methods, vision-based methods have more advantages in fire detection. Existing approaches fail to achieve a good trade-off among fire locations, false alarms and model size. In this paper, we propose a fire detection method based on deep learning with anchor-free architecture. We apply a conditional convolution to boost the visual representation of fire and construct a lightweight backbone by using the proposed conditional ghost module. A color-weighted loss function is presented to obtain more valuable information by using the unique color of fire. Besides, we create an annotated dataset for fire detection. A series of experiments demonstrate that the proposed method outperforms other popular methods based on handcraft features and deep learning object detection. Furthermore, the model size of the proposed method is only 34.92 MB, 80.73% smaller than that of the suboptimal method.

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Authors and Affiliations

  1. School of Microelectronics, Tianjin University, Tianjin, 300072, China

    Rong Zhang, Wei Zhang, Pu Li & Jianhan Zhao

  2. College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, China

    Yanyan Liu

Authors
  1. Rong Zhang
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  2. Wei Zhang
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  3. Yanyan Liu
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  4. Pu Li
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  5. Jianhan Zhao
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Correspondence to Wei Zhang.

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Zhang, R., Zhang, W., Liu, Y. et al. An efficient deep neural network with color-weighted loss for fire detection. Multimed Tools Appl 81, 39695–39713 (2022). https://doi.org/10.1007/s11042-022-12861-9

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  • DOI: https://doi.org/10.1007/s11042-022-12861-9

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