Video Smoke Detection Based on Deep Saliency Network
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Video smoke detection is a promising fire detection method especially in open or large spaces and outdoor environments. Traditional smoke detection consists of candidate region extraction and classification, but it lacks powerful characterization for smoke. In this paper, we propose a novel method for video smoke detection 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 CNNs are combined to extract the informative smoke saliency map. For the need of application for smoke event detection, an end-to-end framework for salient smoke detection and existence prediction of smoke is proposed. The deep feature map is combined with the saliency map to predict the existence of smoke in image. Initial dataset 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 demonstrates the excellent performance of the ultimate framework.
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