吉林大学学报（信息科学版）2025，Vol.43Issue(5)：1014-1024,11.
基于轻量卷积及跨空间学习注意力机制的安全帽佩戴检测模型
Helmet Wearing Detection Model Based on Lightweight Convolution and Cross Spatial Learning Attention Mechanism
摘要
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.关键词
YOLOv8算法/注意力机制/轻量卷积/安全帽佩戴检测Key words
YOLOv8/attention mechanism/lightweight convolution/helmet wearing test分类
计算机与自动化引用本文复制引用
吴湘宁,王梦雪,潘志鹏,方恒,蔡泽宇..基于轻量卷积及跨空间学习注意力机制的安全帽佩戴检测模型[J].吉林大学学报（信息科学版）,2025,43(5):1014-1024,11.基金项目
国家自然科学基金资助项目(U21A2013) （U21A2013）
湖北省自然科学基金资助项目(2021CFB506) （2021CFB506）
