计算机与现代化Issue(9)：79-89,11.DOI:10.3969/j.issn.1006-2475.2025.09.012
基于视频时序特征的多模态隧道火灾检测
Multimodal Tunnel Fire Detection Based on Temporal Features of Video
摘要
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 fea-ture 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 signifi-cantly improves the ability to distinguish between flames and light sources.The MFD method effectively controls the fusion prob-ability below 0.5 in cases of false alarms,while maintaining the probability above 0.5 in fire scenarios.Compared with other meth-ods,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.关键词
隧道火灾检测/视频序列动态特征/多模态融合/YOLOV网络Key words
tunnel fire detection/dynamic features of video sequences/multimodal fusion/YOLOV network分类
信息技术与安全科学引用本文复制引用
杨添顺,宋焕生,梁浩翔,刘浩楠,马辛洲,孙士杰,张绍阳..基于视频时序特征的多模态隧道火灾检测[J].计算机与现代化,2025,(9):79-89,11.基金项目
国家重点研发计划项目(2023YFB4301800) （2023YFB4301800）
国家自然科学基金资助项目(62072053) （62072053）
中国高校产学研创新基金新一代信息技术创新项目(2022IT041) （2022IT041）
国家资助博士后研究人员计划项目(GZC20241447) （GZC20241447）
