FB-YOLOv8s: A fire detection algorithm based on YOLOv8s

Abstract

The significance of fire detection lies in protecting public safety and safeguarding the lives and property of people. However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. To effectively solve these issues, a fire detection algorithm based on YOLOv8s is introduced in this paper, called FB-YOLOv8s. First, the FasterNet lightweight network is introduced into the YOLOv8s network, merging the FasterNet Block structure of FasterNet with the original C2f modules to reduce the number of model parameters. Second, the Bi-directional Feature Pyramid Network (BiFPN) is incorporated to replace the Path Aggregation Network (PANet) in the neck network to enhance the model’s feature fusion capability. Finally, we adopt the WIoUv3 loss function to optimize the training process and improve detection accuracy. The experimental results demonstrate that compared to the original algorithm, the mAP${}_{0.5}$ of FB-YOLOv8s increases by 2.0 %, and the number of parameters decreases by 25.23 %. This method has better detection performance for fire targets.