摘要
Due to natural disaster and global warning, one can expect unexpected fire, which causes panic among people and extent to death. To reduce the impact of fire, the authors propose a new method for predicting and rating fire in video through deep-learning models in this work such that rescue team can save lives of people. The proposed method explores a hybrid deep convolutional neural network, which involves motion detection and maximally stable extremal region for detecting and rating fire in video. Further, the authors propose to use a channel-wise attention mechanism of the deep neural network for detecting rating of fire level. Experimental results on a large dataset show the proposed method outperforms the existing methods for detecting and rating fire in video.
基金
supported by National Key R&D Program of China under Grant no.2018YFC0407901
the Natural Science Foundation of China under Grant Grant no.61702160,Grant 61672273 and Grant no.61832008
the Science Foundation of Jiangsu under Grant BK20170892
the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021
Scientific Foundation of State Grid Corporation of China(Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines)
the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.
