Smoke recognition network based on dynamic characteristics. (1st June 2020)

• Record Type: Journal Article
• Authors: Wang, Dahan; Luo, Sheng; Zhao, Li; Pan, Xiaoming; Wang, Muchou; Zhu, Shunzhi
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 3(2020:May/Jun.)
• Publication Date: 2020-06-01
• Subjects: Smoke detection -- fire alarming -- neural network -- dynamic characteristics -- convolution 3D network -- motion information; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420925662
• Languages: English
• ISSNs: 1729-8806

Abstract

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.