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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.

Health and safety management has been an important issue in construction industry. National regulations impose the using of hard hats in construction sites. However, there are often cases in which the construction workers neglect the regulations. It is desired to monitor the correct wearing of hard hat in real time and explore monitoring techniques facilitated by deep-learning algorithms. In this paper, a convolutional neural network based hard-hat detection algorithm is proposed. In this algorithm, the detection of construction workers and the hard hats are assisted by computer vision technique where deep learning model are trained to identify the proper wearing of hard hats. The optimization of the proposed neural networks can reduce the computational complexity while maintaining a relatively high recognition precision. Experiments have been performed using five different algorithms for comparison and results demonstrate that the proposed algorithm excels in the mAP and FPS performance metrics. The experimental results collected on an embedded platform reveal that the proposed algorithm presents a good candidate for similar applications where real-time deep-learning application is desired.