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Fire detection based on improved PP-YOLO

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Abstract

A fire detection method based on improved PP-YOLO is proposed to promote the performance of flame detection. Specifically, based on PP-YOLO, a feature fusion network is introduced to fuse the two adjacent output feature maps of backbone so that the high-level features can better fuse the details of low-level features. Then, an attention module is employed in the intermediate fusion feature map of two adjacent outputs, which lets the network selectively fuse the valuable information in the feature maps in a self-learning manner to alleviate dilution and aliasing effect of information during feature fusion. Finally, combined with different training tricks, such as data augments and learning rate adjustment strategy, the model is trained and tested on two testing sets. The experiment results demonstrate that the improved model can achieve 86.87% and 85.66% mean average precision (mAP) on two testing sets. Additionally, the precision of improved model is 97.23% and 93.39%, and the false alarm rate can achieve 0.83% and 1.68%, respectively. And the average detection time is 25.62 ms. In conclusion, the model is suitable for various fire scenarios and can be well utilized in actual conditions.

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Funding

This study was funded by Project on the Integration of Industry, Education and Research of Fujian Province, China [No. 2021Y4004], and Natural Science Foundation of Fujian Province [No. 2020J05102].

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China

    Chuangmao Chen, Fuqiang Lai, Guoqiang Zheng & Youxi Lin

  2. Mechanical and Electrical Engineering Practice Center, Fuzhou University, Fuzhou, 350116, China

    Jie Yu

  3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yuqing Lin

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  1. Chuangmao Chen
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  2. Jie Yu
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  3. Yuqing Lin
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  4. Fuqiang Lai
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  6. Youxi Lin
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Correspondence to Youxi Lin.

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Chen, C., Yu, J., Lin, Y. et al. Fire detection based on improved PP-YOLO. SIViP 17, 1061–1067 (2023). https://doi.org/10.1007/s11760-022-02312-1

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  • DOI: https://doi.org/10.1007/s11760-022-02312-1

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