Multi-Scale Prediction For Fire Detection Using Convolutional Neural Network

The automation of fire detection systems can reduce the loss of life and property by allowing a fast and accurate response to fire accidents. Although visual techniques have some advantages over sensor-based methods, conventional image processing-based methods frequently cause false alarms. Recent studies on convolutional neural networks have overcome these limitations and exhibited an outstanding performance in fire detection tasks. Nevertheless, previous studies have only used single-scale feature maps for fire image classification, which are insufficiently robust to fires of various sizes in the images. To address this issue, we propose a multi-scale prediction framework that exploits the feature maps of all the scales obtained by the deeply stacked convolutional layers. To utilize the feature maps of various scales in the final prediction, this paper proposes a feature-squeeze block. The feature-squeeze block squeezes the feature maps spatially and channel-wise to effectively use the information from the multi-scale prediction. Extensive evaluations demonstrate that the proposed method outperforms the state-of-the-art convolutional neural networks-based methods. As a result of the experiment, the proposed method shows 97.89% for F1-score and 0.0227 for false positive rate in the average of evaluations for multiple.