Fire Detection Method Based on Deep Residual Network and Multi-Scale Feature Fusion

Fire can cause serious damage to the natural environment and social economy, if it is not intervened early. Therefore, an effective fire detection method is significant and helpful. In this paper, a fire detection method based on deep learning is proposed to detect fire and smoke. Firstly, the residual network structure is applied to extract depth feature of the image. The problem of shallow features easily disappearing is solved with improved ResNet-50. A network composed of multiple BiFPN modules is established for multi-scale feature fusion and enhancement. Intersection over Union and cross entropy are applied to predict the scope and category of boundary boxes. Finally, the prediction results are obtained by comparing the confidence of the bounding box. Experimental results show that this method performs better in running time and accuracy than the existing detection networks. The feasibility of this method is verified in the field of fire detection.