  Video smoke detection is a promising fire detection method especially in open
or large spaces and outdoor environments. Traditional video smoke detection
methods usually consist of candidate region extraction and classification, but
lack powerful characterization for smoke. In this paper, we propose a novel
video smoke detection method based on deep saliency network. Visual saliency
detection aims to highlight the most important object regions in an image. The
pixel-level and object-level salient convolutional neural networks are combined
to extract the informative smoke saliency map. An end-to-end framework for
salient smoke detection and existence prediction of smoke is proposed for
application in video smoke detection. The deep feature map is combined with the
saliency map to predict the existence of smoke in an image. Initial and
augmented dataset are built to measure the performance of frameworks with
different design strategies. Qualitative and quantitative analysis at
frame-level and pixel-level demonstrate the excellent performance of the
ultimate framework.
