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http://dx.doi.org/10.3837/tiis.2020.09.008

Video smoke detection with block DNCNN and visual change image  

Liu, Tong (College of Electronic Science, National University of Defense Technology)
Cheng, Jianghua (College of Electronic Science, National University of Defense Technology)
Yuan, Zhimin (Naval University of Engineering)
Hua, Honghu (College of Electronic Science, National University of Defense Technology)
Zhao, Kangcheng (College of Electronic Science, National University of Defense Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3712-3729 More about this Journal
Abstract
Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.
Keywords
smoke detection; deep learning; object detection; convolutional neural networks;
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