A Survey on Video Smoke Detection

2017 ◽  
pp. 211-222 ◽  
Author(s):  
Princy Matlani ◽  
Manish Shrivastava
Keyword(s):  
Smoke Detection

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

Efficient attention based deep fusion CNN for smoke detection in fog environment

2021 ◽  
Vol 434 ◽  
pp. 224-238
Author(s):  
Lijun He ◽  
Xiaoli Gong ◽  
Sirou Zhang ◽  
Liejun Wang ◽  
Fan Li
Keyword(s):  
Smoke Detection

Real-Time Image-based Smoke Detection in Endoscopic Videos

2017 ◽  
Author(s):  
Andreas Leibetseder ◽  
Manfred Jürgen Primus ◽  
Stefan Petscharnig ◽  
Klaus Schoeffmann
Keyword(s):  
Real Time ◽  
Smoke Detection ◽  
Real Time Image ◽  
Time Image ◽  
Endoscopic Videos

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

Dynamic fire and smoke detection and classification for flashover prediction

2021 ◽  
Author(s):  
Alexander L. Huyen ◽  
Kyongsik Yun ◽  
Sky De Baun ◽  
Samuel Wiggins ◽  
Jessi Bustos ◽  
...  
Keyword(s):  
Smoke Detection

Smoke Detection Based on a Semi-supervised Clustering Model

2014 ◽  
pp. 291-298 ◽  
Author(s):  
Haiqian He ◽  
Liqun Peng ◽  
Deshun Yang ◽  
Xiaoou Chen
Keyword(s):  
Smoke Detection ◽  
Supervised Clustering ◽  
Clustering Model

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.

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