Fire smoke detection based on target-awareness and depthwise convolutions

2021 ◽  
Author(s):  
Yunji Zhao ◽  
Haibo Zhang ◽  
Xinliang Zhang ◽  
Xiangjun Chen
Keyword(s):  
Smoke Detection ◽  
Fire Smoke

Research on Fire Smoke Detection Method Based on Video Image

2021 ◽  
Author(s):  
Xiaobo Xu ◽  
Guoxuan Tang ◽  
Jiayi Wu ◽  
Changzhou Geng
Keyword(s):  
Detection Method ◽  
Video Image ◽  
Smoke Detection ◽  
Fire Smoke

scholarly journals A Video Based Fire Smoke Detection Using Robust AdaBoost

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3780 ◽  
Author(s):  
Xuehui Wu ◽  
Xiaobo Lu ◽  
Henry Leung
Keyword(s):  
Smoke Detector ◽  
Dynamic Features ◽  
Smoke Detection ◽  
Motion Direction ◽  
Direction Change ◽  
Satisfactory Performance ◽  
Orientation Histogram ◽  
Fire Smoke ◽  
Motion Speed ◽  
Camera Sensors

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

scholarly journals Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

2015 ◽  
Vol 7 (4) ◽  
pp. 4473-4498 ◽  
Author(s):  
Xiaolian Li ◽  
Weiguo Song ◽  
Liping Lian ◽  
Xiaoge Wei
Keyword(s):  
Neural Network ◽  
Forest Fire ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Smoke Detection ◽  
Modis Data ◽  
Fire Smoke

scholarly journals Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Ching Ho
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Time Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Large Fires ◽  
Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

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