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Light-FireNet: an efficient lightweight network for fire detection in diverse environments

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Abstract

Fire and smoke detection using deep learning have recently proven to be a robust and efficient detection approach in contrast to traditional vision-based techniques. Efforts are made by researchers to leverage this promising direction but are always faced with a trade-off between performance accuracy and model size. To tackle this, we present Light-FireNet, an enhanced lightweight, fast, and cost-effective system based on a combination of lighter convolution mechanisms inspired by Hard Swish (H-Swish), and a novel architectural design built from scratch. Experimental results and performance analysis reveal that our proposed method has 32% fewer parameters than AlexNet, 3.03 MB lighter than MobileNetV2, and achieves a better detection accuracy of 97.83%, which is higher than most existing fire detection techniques in the literature.

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  1. http://nnmtl.cn/EFDNet/

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 62071396.

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Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610031, China

    Otabek Khudayberdiev, Jiashu Zhang & Sheng Zhang

  2. College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Sani M. Abdullahi

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  1. Otabek Khudayberdiev
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  2. Jiashu Zhang
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  3. Sani M. Abdullahi
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  4. Sheng Zhang
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Correspondence to Sani M. Abdullahi.

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Khudayberdiev, O., Zhang, J., Abdullahi, S.M. et al. Light-FireNet: an efficient lightweight network for fire detection in diverse environments. Multimed Tools Appl 81, 24553–24572 (2022). https://doi.org/10.1007/s11042-022-12552-5

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