Intelligent tunnel fire emergency linkage measures based on image recognition

Yanjun Jin; Futao Wu; Xihao Zhu; Hancheng Yu; Minyao Xia; Haiping Liu

doi:10.1117/12.3021308

19 February 2024 Intelligent tunnel fire emergency linkage measures based on image recognition

Yanjun Jin, Futao Wu, Xihao Zhu, Hancheng Yu, Minyao Xia, Haiping Liu

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 1306305 (2024) https://doi.org/10.1117/12.3021308
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

An accurate and fast fire smoke detection algorithm is urgently needed to solve the emergency linkage measures to prevent early fire spread and after the fire. In this paper, the depth matrix of motion difference is constructed for background modeling, the moving target is extracted using flame smoke motion characteristics, and the suspected area of flame smoke is obtained through corrosion and expansion. The unique characteristics of flame and smoke are then extracted, and the smoke flame recognition model is built using a BP neural network optimized by a genetic algorithm. The experimental results show that the algorithm can reliably eliminate the interference of moving vehicles and lights in the tunnel, detect flames and smoke, and generate an alarm, and that it can be applied to fire smoke detection in real-world scenes. Finally, the emergency linkage measures in the corresponding area are automatically initiated based on the position of the fire point via information interaction with the fire smoke detection and alarm system.

Citation Download Citation

Yanjun Jin, Futao Wu, Xihao Zhu, Hancheng Yu, Minyao Xia, and Haiping Liu "Intelligent tunnel fire emergency linkage measures based on image recognition", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 1306305 (19 February 2024); https://doi.org/10.1117/12.3021308

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