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Optimized Deep Learning Model for Fire Semantic Segmentation

Songbin Li1,*, Peng Liu1, Qiandong Yan1, Ruiling Qian2

1 Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China
2 Loughborough University, Loughborough, LE11 3TT, United Kingdom

* Corresponding Author: Songbin Li. Email: email

Computers, Materials & Continua 2022, 72(3), 4999-5013.


Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset of low-level features. Besides, an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results. We compare the proposed method with existing advanced semantic segmentation and salient object detection methods. Experimental results demonstrate that the proposed method achieves 94.1%, 93.6%, 94.6%, 95.3%, and 95.9% Intersection over Union (IoU) on five test sets respectively which outperforms the suboptimal method by a large margin. In addition, in terms of accuracy, our approach also achieves the best score.


Cite This Article

APA Style
Li, S., Liu, P., Yan, Q., Qian, R. (2022). Optimized deep learning model for fire semantic segmentation. Computers, Materials & Continua, 72(3), 4999-5013.
Vancouver Style
Li S, Liu P, Yan Q, Qian R. Optimized deep learning model for fire semantic segmentation. Comput Mater Contin. 2022;72(3):4999-5013
IEEE Style
S. Li, P. Liu, Q. Yan, and R. Qian "Optimized Deep Learning Model for Fire Semantic Segmentation," Comput. Mater. Contin., vol. 72, no. 3, pp. 4999-5013. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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