An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition

Detecting forest fire smoke during the initial stages is vital for preventing forest fire events. Recent studies have shown that exploring spatial and temporal features of the image sequence is important for this task. Nevertheless, since the long distance wildfire smoke usually move slowly and lacks salient features, accurate smoke detection is still a challenging task. In this paper, we propose a novel Attention Enhanced Bidirectional Long Short-Term Memory Network (ABi-LSTM) for video based forest fire smoke recognition. The proposed ABi-LSTM consists of the spatial features extraction network, the Bidirectional Long Short-Term Memory Network(LSTM), and the temporal attention subnetwork, which can not only capture discriminative spatiotemporal features from image patch sequences but also pay different levels of attention to different patches. Experiments show that out ABi-LSTM is capable of achieving best accuracy and less false alarms on different types of scenarios. The ABi-LSTM model achieve a highly accuracy of 97.8%, and there is 4.4% improvement over the image-based deep learning model.