An improved flame smoke detection algorithm for YOLOv8n

Given the problems of a large number of flame and smoke detection parameters and poor detection effect in the current fire warning system, this paper proposes a lightweight YOLOv8n improved algorithm. First, in terms of data sets, the Mosaic data enhancement method is used to increase the diversity of data. Secondly, the C2f module in the Backbone part is fused with the LSK attention mechanism to form a C2f-LSK module, which can increase the ability to extract flame and smoke features in complex fire scenes, thereby improving the detection accuracy. Then, for the Neck part, the weighted Bidirectional Feature Pyramid (BiFPN) is used to replace the original Path Aggregation Network (PANet) of YOLOv8, which promotes the fusion of feature maps of different scales, thereby improving the detection accuracy of the model. Experiments show that the improved YOLOv8n has high accuracy in flame and smoke detection, with precision and [email protected] reaching 80.8% and 75.3% respectively, which are 3.8% and 3% higher than the original model. The improved YOLOv8n network model has higher accuracy and lower false alarm rates in flame and smoke detection.