Using knowledge inference to suppress the lamp disturbance for fire detection  

作　　者：Kailai Sun Qianchuan Zhao Xinwei Wang 

机构地区：[1]Center for Intelligent and Networked Systems,Department of Automation,BNRist,Tsinghua University,Beijing,China

出　　处：《Journal of Safety Science and Resilience》2021年第3期124-130,共7页安全科学与韧性（英文）

基　　金：This work is supported by Key R&D Project of China under Grant No.2017YFC0704100,2016YFB0901900;National Natural Science Foun-dation of China under Grant No.61425024,the 111 International Col-laboration Program of China under Grant No.BP2018006;2019 Major Science and Technology Program for the Strategic Emerging Industries of Fuzhou under Grant No.2019-Z-1;in part by the BNRist Pro-gram under Grant No.BNR2019TD01009;the National Innovation Cen-ter of High Speed Train R&D project(CX/KJ-2020-0006).

摘　　要：Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and fire.Recently,research on convolutional neural networks(CNN)for fire image detection has become a hot topic.However,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm rate.Besides,light and lamps often exist in buildings.To address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp disturbance.Firstly,we train the YoloV3 network to detect and recognize lamps.Secondly,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation method.Last,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 networks.The experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at https://github.com/kailaisun/fire-detection-without-lamp.

关 键 词：fire detection LAMP prior knowledge causal inference convolutional neural networks 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] X928.7[自动化与计算机技术—计算机科学与技术]