桂林电子科技大学学报2025，Vol.45Issue(1)：41-48,8.DOI:10.16725/j.1673-808X.202315
融合自注意力机制的YOLOv5交通标志检测算法
A YOLOv5 traffic sign detection algorithm of fusion self-attention mechanism
摘要
Abstract
Aiming at the problems of insufficient global feature information,insufficient feature fusion and low detection efficiency of YOLOv5 network model in complex environment.A kind of YOLOv5 traffic sign detection algorithm incorporating self-atten-tion mechanism was proposed.In the backbone network feature extraction part,the Swin-Transformer module based on the self-at-tention mechanism and the C3 module which can reduce the calculation amount of the model to increase the information interaction between the feature images to obtain multi-scale image features.The feature image processing part of the model uses the visual Transformer model and the Swin-Transformer module to fuse the feature images,obtains the global feature information of the im-age to be measured,and improves the detection accuracy of the model.Finally,the original feature image splicing mode is weighted for processing,and the important traffic sign feature information can be preferentially detected,which improves the detection effi-ciency of the model.After testing in the TT100K datasets,the final mean average detection accuracy reached 83.51%,which was 2.50 percentage points higher than the original YOLOv5 network model and 0.037s higher compared to the original single feature image detection rate.The experimental results show that the YOLOv5 model integrating the self-attention mechanism effectively im-proves the global feature extraction ability,detection accuracy and detection efficiency of traffic sign detection.关键词
交通标志检测/自注意力机制/特征融合/YOLOv5/Swin-TransformerKey words
traffic sign detection/self attention mechanism/feature fusion/YOLOv5/Swin-Transformer分类
信息技术与安全科学引用本文复制引用
那万达,张向利..融合自注意力机制的YOLOv5交通标志检测算法[J].桂林电子科技大学学报,2025,45(1):41-48,8.基金项目
广西无线宽带通信与信号处理重点实验室主任基金(GXKL06200104) （GXKL06200104）
广西云计算与大数据协同创新中心(YD1904) （YD1904）
