Video smoke detection based on deep saliency network

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 the existence prediction of smoke is proposed for application in video smoke detection. A deep feature map is combined with a saliency map to predict the existence of smoke in an image. Initial and augmented datasets are built to measure the performance of frameworks with different design strategies. Qualitative and quantitative analyses at the frame-level and pixel-level demonstrate the excellent performance of the ultimate framework.