Abstract: To improve the efficiency and accuracy of the helmet wearing detection model, the LFE-Y8 (LightConv, Focal Loss and EMA Attention You Only Look Once version 8) model is proposed. This model adopts the Focal Loss function to solve the problem of imbalanced sample categories. The original model is optimized using LightConv lightweight convolution, which improves the feature extraction ability. In order to better focus on small targets, an efficient multi-scale EMA (Efficient Multi Scale Attention) attention mechanism for cross spatial learning is integrated. The experimental results show that the LFE-Y8 model effectively improves the accuracy of helmet wearing detection compared to the improved YOLOv8 model. The improved algorithm has an accuracy increase of 0. 6% and a recall increase of 2. 1%. The mAP@ 50 is improved by 1.2%, and mAP@ 50-95 is improved by 1.5%, demonstrating the effectiveness of the LFE-Y8 model in practical applications. 

Key words: attention mechanism, lightweight convolution, helmet wearing test