Deep spatial-temporal networks for flame detection

Abstract

Every year, fire accidents cause substantial economic losses and casualties. Being able to detect a fire at the early stage is the only way to avoid notable disasters. Although conventional fire alarm systems (CFAs) that depend on heat and flame sensors are used for a fire safety-catch in our society, they cannot be used effectively for large and open spaces due to performance parameters of the sensors. Recently, most of the state-of-the-art methods for fire detection are evolving based on deep learning (DL) technique. However, it is a difficult task to detect fire from visual scenes due to significant irregularities in the color, size, form, texture and flickering frequency of fire. In the present work, we proposed a two-stage cascaded architecture to improve accuracy. In the first stage, we introduced the Spatio-Temporal network, which efficiently and effectively combines both shape and motion flicker based characteristics to obtain heatmaps of fire regions in the input images. By analyzing the heatmaps with a threshold segmentation method, the candidate of the fire region in the input image can be automatically located. Besides, to minimize false-positive due to some object similar to flame, in the second stage, original image and heatmaps of candidate region are fused for improving abilities of classifier to distinguish whether it is a fire or not. Also, the center loss function is adopted to backpropagate fused features to overcome the impact of intraclass heterogeneity on the representation of features. Furthermore, we tested the proposed method on three different datasets, and the results of our experiments reveal that the proposed method has achieved better performance than the other existing state-of-the-art methods.