Computer Science

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

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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
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