Computer Science ›› 2019, Vol. 46 ›› Issue (11): 291-296.doi: 10.11896/jsjkx.180901640

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Fire Images Features Extraction Based on Improved Two-stream Convolution Network

XU Deng1,2, HUANG Xiao-dong3   

  1. (Jiangsu Internet of Things and Manufacturing Information Engineering Research Center,Changzhou,Jiangsu 213164,China)1
    (School of Information Engineering,Changzhou Vocational Institute of Mechatronic Technology,Changzhou,Jiangsu 213164,China)2
    (Key Laboratory of MEMS of Ministry of Education,Southeast University,Nanjing 210096,China)3
  • Received:2018-09-27 Online:2019-11-15 Published:2019-11-14

Abstract: Fire detection based on image processing technology is an important branch in the field of fire monitoring in recent years.Aiming at the fire detection of open environment,using the dynamic and static characteristics of smoke and flame generated during the fire,the two-stream convolutional neural network is used as the theoretical basis to detect the fire.The two-stream convolutional neural network uses spatial and temporal streams to extract spatial information and temporal information in the video respectively.However,the information in the early stage of the fire is weak and the features are not obvious enough.In order to improve the initial recognition rate,a spatial enhancement network was proposed as the spatial stream of the two-stream convolutional neural network to extract and enhance the spatial information of the video.The spatial enhancement network simultaneously convolves the current frame Vt and the previous frame Vt-1,subtracting the convolution features of the Vt image with the convolution features of the Vt-1 image,to preserve the difference of the convolution features,and adding the convolution features difference to the convolution features of the current frame Vt,thereby enhance the spatial features convolution of the current frame Vt-1.Temporal stream of two-stream convolutional network convolves the optical flow image Vt of the current frame to get the temporal features.Finally,the enhanced spatial and temporal features are fused to classify.The experimental results show that the improved two-stream convolutional network has a 6.2% higher recognition rate than the original two-stream convolutional network,and achieved 92.15% recognition rate on the public dataset,indicating the effectiveness and superiority of the proposed method.Comparing with other methods,the network structure is designed lower but achieves good results,improves the identification accuracy of fire and smoke as well as realizes the early warning of fire,shorten detection time.

Key words: Open environment fire detection, Optical flow method, Spatial enhancement network, Spatial temporal features fusion, Two-stream convolutional network

CLC Number: 

  • TP181
[1]LI H Y.Research on large space fire monitoring technologybased on image processing [D].Chongqing:Chongqing University,2008.(in Chinese)
[2]FERNANDEZ A,ALVAREZ M X,BIANCONI F.Texture description through histograms of equivalent patterns [J].Journal of Mathematical Imaging and Vision,2013,1(4):76-102.
[3]AHMED A.Machine Learning Approach for Adaptive Automated Smoke Region Detection [D].Swiss Federal Institute of Techology Zurich,2015:1735-1780.
[4]YANG J,CHEN F,ZHANG W.Visual-based smoke detection using support vector machine[C]∥2008 Fourth International Conference on Natural Computation.IEEE,2008:301-305.
[5]TUNG T X,KIM J M.An effective four-stage smoke-detectionalgorithm using video images for early fire-alarm systems [J].Fire Safety Journal,2011,46(5):276-282.
[6]ZHANG Q,XU J,XU L.Deep Convolutional Neural Networks for Forest Fire Detection[C]∥2016 International Forum on Management,Education and Information Technology Application.2016[7] XU Z G,XU J L.Automatic Fire Smoke Detection Based on Image Visual Features [C]∥International Conference on Computational Intelligence and Security Workshops (CISW 2007).2007:316-319[8]ZHAO L.Research on video-based fire smoke detection algorithm [D].Xiamen:Huaqiao University,2017 (in Chinese)
[9]NODA S,UEDA K.Fire detection in tunnels using an image processing method[C]∥Proceeding of Vehicle Navigation and Information Systems Conference.1994:57-62.
[10]XU W S,TIAN C Z,FANG S M.Automatic fire identification based on image visual features [J].Computer Engineering,2003,29(18):112-113.(in Chinese)
[11]SONG W G,FAN W C,WU L B.Fire images detection method based on artificial neural network [J].Fire Science,1999(3):49-56.(in Chinese)
[12]SUN C.Research and Design of Fire Detection Algorithm Based on Video Image [D].Jinan:Shandong University,2018.(in Chinese)
[13]TAO C,ZHANG J,WANG P.Smoke Detection Based on Deep Convolutional Neural Networks[C]∥International Conference on Industrial Informatics Computing Technology.2017:150-153.
[14]HORN B,SCHUNCK B.Determining optical flow[J].ArtificialIntelligence,1981,17:185-203.
[15]MEMIN E,PEREZ P.Hierarchical estimation and segmentation of dense motion fields [J].International Journal of Computer Vision,2002,46(2):129-155[16]ZHANG J H,ZHUANG J,DU H F,et al.One fire flame recognition algorithm based on video multi-feature fusion [J].Journal of Xi’an Jiaotong University,2006,40(7):811-814.(in Chinese)
[17]KOSIOREK A.神经网络中的注意力机制[J].机器人产业,2017(6):14-19.
[18]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolution neural networks[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:1097-1105.
[19]SIMONYAN K,ZISSERMAN A.Two-Stream ConvolutionalNetworks for Action Recognition in Videos [J].Computational Linguistics,2014,1(4):568-576.
[20]JIA Y,SHELHAMER E,DONAHUE J,et al.Caffe:Convolutional architecture for fast feature embedding[C]∥Proceedings of the 22nd ACM international conference on Multimedia.ACM,2014:675-678.
[21]DONAHUE J,ANNE HENDRICKS L,GUADARRAMA S,et al.Long-Term Recurrent Convolutional Networks for Visual Recognition and Description[C]∥IEEE Conference on ComputerVision and Pattern Recognition (CVPR).2015:2625-2634.
[22]DENG J,DONG W,SOCHER R,et al.ImageNet:A large-scale hierarchical image database[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2009.
[23]SHARMA J,GRANMO O C,GOODWIN M,et al.Deep Convolutional Neural Networks for Fire Detection in Images[C]∥Engineering Applications of Neural Networks(EANN 2017).Cham:Springer,2017:183-193.
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