[1] Xu Zhao, Dai Tianqi,. Efficient fire-smoke detection and evacuation simulation from buildings based on YOLOX-Swin [J]. Journal of Southeast University (English Edition), 2023, 39 (4): 372-383. [doi:10.3969/j.issn.1003-7985.2023.04.006]

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Efficient fire-smoke detection and evacuation simulation from buildings based on YOLOX-Swin()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

39

Issue:

2023 4

Page:

372-383

Research Field:

Civil Engineering

Publishing date:

2023-12-20

Info

Title:

Efficient fire-smoke detection and evacuation simulation from buildings based on YOLOX-Swin

Author(s):

Xu Zhao;  Dai Tianqi

School of Civil Engineering, Southeast University, Nanjing 211189, China

Keywords:

computer vision;  self-attention;  ant colony algorithm;  fire dynamics simulator(FDS)

PACS:

TU998.1;TP391.9

DOI:

10.3969/j.issn.1003-7985.2023.04.006

Abstract:

To achieve efficient emergency response and evacuation from building fires, the possibility of applying object detection technology to building fire emergency management is investigated. An application of object detection algorithms in the early warning stage of fire is proposed by combining a transformer, convolutional neural network, and lightweight attention mechanism module(namely convolutional block attention module)to extract the local and global features of flames and smoke, thereby improving the accuracy of the object detection algorithm and achieving fast localization of fire occurrence. An improved ant colony algorithm for path searching is proposed by improving the heuristic function and pheromone evaporation coefficient. A grid map model is developed in a case, and the effectiveness of the proposed method is verified through simulation and emulation by considering positioning information. The results show that compared with the YOLOX algorithm, the YOLOX-Swin model improves the average accuracy by 1.5%. The improved ant colony algorithm reduces the search range of the traditional ant colony algorithm, improves the convergence speed of the model, and avoids the problem of getting trapped in local optimum solutions. By integrating early warning of fire and personnel evacuation, a comprehensive building fire emergency management plan is developed.

References:

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Memo

Memo:

Biographies: Xu Zhao(1982—), male, Ph.D., associate professor, xuzhao@seu.edu.cn.
Foundation items: The National Natural Science Foundation(No. 72071043), the Natural Science Foundation of Jiangsu Province(BK20201280), Humanities and Social Science Fund of Ministry of Education(20YJAZH114).
Citation: Xu Zhao, Dai Tianqi.Efficient fire-smoke detection and evacuation simulation from buildings based on YOLOX-Swin[J].Journal of Southeast University(English Edition), 2023, 39(4):372-383.DOI:10.3969/j.issn.1003-7985.2023.04.006.

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