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.
Highlights
About 6–7 million times of fires happen every year, which cause huge economic losses
We describe a brief state of the art in smoke dynamic characteristics
After synthesizing the edge blurring, the gradual changing of smoke energy and the gradual changing of smoke color, Lee et al adopted support vector machine (SVM) to make decisions.[20]
Summary
About 6–7 million times of fires happen every year, which cause huge economic losses. Such static characteristics as chrominance,[2,3] texture,[4,5,6] shape,[7] frequency,[9] and transparency[19] are adopted to detect smoke, but the wide-ranging varieties of these characteristics make the recognition difficult.[15] Even some of these characteristics are synthesized with machine learning methods.[4,8,19,20,21,22,23,24] Based on surface wave and Markov tree model to extract smoke textures, Ye et al adopted support vector machine (SVM) to judge whether there is any smoke.[4] After synthesizing the edge blurring, the gradual changing of smoke energy and the gradual changing of smoke color, Lee et al adopted SVM to make decisions.[20] After spectral features are extracted, Li et al.[25] classified smoke, cloud, and underlying surface by a back-propagation neural network.
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