Research on YOLOv8 Detection Algorithm Based on SKAttention Fusion

This study proposes a YOLOv8 model that integrates SKAttentionn mechanism to improve the recognition ability of flames for object detection algorithms. This article analyzes the challenges and requirements of eliminating safety hazards in the current production environment, and introduces the relevant structure of the YOLOv8 network model and the principle of the SKAttentionn mechanism. The model was validated using a self built standard flame dataset, and comparative experiments with ECA and GAM modules were also added to evaluate the detection performance of the YOLOv8 model with SKAttentionn mechanism. The experimental results show that the detection accuracy of the improved YOLOv8 model is higher than that of the original model and the model with ECA and GAM added, which has certain application value.