基于计算机视觉的轨道交通站内火灾检测与定位  

Computer Vision-based Fire Detection and Localization Inside Urban Rail Transit Stations

作　　者：张金雷 杨健 刘晓冰 陈瑶[1] 杨立兴[1] 高自友[1] ZHANG Jinlei;YANG Jian;LIU Xiaobing;CHEN Yao;YANG Lixing;GAO Ziyou(School of Systems Science,Beijing Jiaotong University,Beijing 100044,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学系统科学学院,北京100044 [2]北京交通大学交通运输学院,北京100044

出　　处：《交通运输系统工程与信息》2024年第3期53-63,共11页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家自然科学基金(72201029,72288101,72322022)。

摘　　要：为及时有效地处理轨道交通站内火灾事件,本文提出基于计算机视觉的站内火灾检测与精细化火灾定位模型(Fire-Detect)。首先,基于Unity仿真模拟和收集互联网图像数据的方式制作站内火灾图像与视频数据集Fire-Rail,用于训练构建的火灾检测算法和精细化火灾定位算法;其次,基于卷积神经网络、残差结构与通道注意力机制构建火灾检测算法,用于检测站内监控视频中每帧分别为“正常状态”或“疑似火灾”状态;最后,在“疑似火灾”状态下,模型启动精细化火灾定位算法,将图像以及后续的每帧图像输入精细化火灾定位算法中,并实时输出火灾发生场景下的精细化火灾定位信息。在Fire-Rail数据集上进行实验,火灾检测算法在测试集的准确率为95.12%;此外,卷积神经网络层级实验平衡了资源消耗和准确率,消融实验验证了各部分的有效性,鲁棒性实验表明,该算法能处理大部分噪声,整体模型的平均火灾定位检测精度mAP为77.3%,可应用于轨道交通站内视频监控设备。To efficiently address the occurrence of in-station fire incidents in rail transit,this paper proposes a computer vision-based model for fire detection and precise fire localization within the rail stations,which is referred to as Fire-Detect.First,this study created the Fire-Rail dataset using the Unity simulation and collecting internet images,which established the dataset to train the fire detection and precise localization algorithms.Then,a fire detection algorithm was developed to integrate convolutional neural networks,residual structures,and channel attention mechanisms.This algorithm classifies each frame of surveillance video within the station as either"normal"or"suspected fire"status.In the"suspected fire"status,the model activates the precise localization algorithm.It processes the"suspected fire"image along with subsequent frames,providing real-time,detailed fire localization information to station attendants.Experimental results on the Fire-Rail dataset demonstrated a fire detection accuracy of 95.12%on the test set.Furthermore,hierarchical experiments with convolutional neural network layers balance the resource consumption and accuracy.Ablation experiments confirmed the effectiveness of individual components,and robustness experiments indicated the algorithm's ability to handle most noise.The overall model achieves an average fire localization detection accuracy(mAP)of 77.3%and is suitable for deployment in video surveillance equipment within rail transit stations.

关 键 词：智能交通 火灾检测 深度学习 轨道交通车站 计算机视觉 

分 类 号：U231.4[交通运输工程—道路与铁道工程]