Open Access

ARTICLE

Optimized Deep Learning Model for Fire Semantic Segmentation

Songbin Li^1,*, 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:

Computers, Materials & Continua 2022, 72(3), 4999-5013. https://doi.org/10.32604/cmc.2022.026498

Received 28 December 2021; Accepted 11 March 2022; Issue published 21 April 2022

Abstract

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.

---

Keywords

---