Research on meteorological equipment recognition and monitoring algorithm
In sparsely populated areas,the monitoring and inspection of meteorological equipment face many challenges,including the remote placement of equipment and the lack of real-time inspection.To solve this problem,this paper proposes the YOLOv8+SPPFCSPC model based on YOLOv8,which has excellent performance in the field of image recognition.By replacing the original SPPF layer with the SPPFCSPC layer,the computational load is successfully reduced,significantly improving the detection speed of meteorological equipment.To further improve the performance of the model in complex environments,the YOLOv8-SA model is proposed.By adding a multi-head self-attention mechanism to Backbone,the correlation between different regions in the image is more accurately captured,effectively improving the accuracy of the model.To validate effectiveness of the model,a specialized meteorological equipment dataset was created and comparative experiments were conducted.The experimental results show that the YOLOv8-SA model proposed in this paper has achieved significant improvements in detection speed and accuracy,with a detection accuracy of 98.6%in the self-made dataset.Compared with the traditional YOLOv8 model,the detection accuracy has increased by 0.6%.This model can effectively solve the monitoring problem of meteorological equipment in sparsely populated areas,providing new ideas for improving the practicality and efficiency of monitoring systems.
万方数据
维普
