Fire detection based on MobileNetv3-YOLOv4

To achieve accurate and rapid fire detection, this paper improves the network structure of YOLOv4 through various means. Given the numerous parameters of the original YOLOv4 network model, we introduce the lightweight MobileNetv3 backbone network to replace the original backbone, thereby reducing network complexity. Additionally, we enhance detection accuracy by incorporating the Inverted-bneck-shortcut structure in the prediction network to facilitate multi-scale feature fusion and prediction. Since publicly available fire image datasets are insufficient for training deep neural networks, we collect fire images through various methods and further expand the dataset using data augmentation techniques, ultimately creating our own fire image dataset. Experimental results demonstrate that the improved YOLOv4 fire detection model presented in this paper is approximately 1/3 the size of the original YOLOv4 model, with a nearly 40% increase in inference speed and a 6% improvement in algorithm accuracy. This suggests that the proposed MobileNetv3-YOLOv4-based fire detection algorithm can accurately and rapidly identify and locate fire targets in images.