Multimodal Tunnel Fire Detection Based on Temporal Features of Video

doi:10.3969/j.issn.1006-2475.2025.09.012

Abstract

Abstract: Abstract: The tunnel environment is closed and narrow， and once a fire occurs， the fire spread and harmful gas generation will seriously threaten the safety of life and property. Existing tunnel fire detection methods based on single-frame images often struggle to accurately distinguish between flames and flame-like light sources. To address this issue， a multi-frame sequence feature extraction method based on the YOLOV network is proposed， which utilizes the dynamic feature variations of targets in video sequences. The VSDFD module is designed to differentiate between flames and light sources by analyzing the feature similarity of adjacent interval frames. In addition， combined with the ambient temperature information collected by the temperature sensor， an MFD multi-mode fusion method is proposed by using DST evidence theory and its derivation method TBM， which is used to calculate the fire probability and realize the tunnel fire detection. The experimental results show that the VSDFD module significantly improves the ability to distinguish between flames and light sources. The MFD method effectively controls the fusion probability below 0.5 in cases of false alarms， while maintaining the probability above 0.5 in fire scenarios. Compared with other methods， the proposed approach achieves an average improvement of 2.8 percentage points in detection accuracy， a 2.7 percentage points reduction in the missed detection rate， and a 5.2 percentage points decrease in the false detection rate. Experiments in various real tunnel fire scenarios verified the accuracy of the proposed method in fire detection.

Key words: Key words: tunnel fire detection, dynamic features of video sequences, multimodal fusion, YOLOV network

CLC Number:

中图分类号：TP391

YANG Tianshun1, SONG Huansheng1, LIANG Haoxiang2, LIU Haonan1, MA Xinzhou1, SUN Shijie1, ZHANG Shaoyang1. Multimodal Tunnel Fire Detection Based on Temporal Features of Video[J]. Computer and Modernization, 2025, 0(09): 79-89.

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