重庆邮电大学学报（自然科学版）2026，Vol.38Issue(1)：83-92,10.DOI:10.3979/j.issn.1673-825X.202412060285
多尺度融合的轻量化交通标志检测算法
Lightweight traffic sign detection algorithm based on multi-scale fusion
摘要
Abstract
To address the challenges in traffic sign detection for autonomous driving,such as small target sizes,large scale variations,low detection accuracy,excessive parameters,and limited deployability,a lightweight traffic sign detection algorithm with multi-scale feature fusion is proposed based on YOLOv8n.The algorithm constructs a lightweight feature extraction module using partial convolution and gated linear units to enable dynamic feature selection.A multi-scale feature fusion module is designed by incorporating dynamic sampling and a channel attention mechanism to effectively fuse features at different levels.In addition,a lightweight downsampling module is built using group convolution and local attention,achieving a balance between computational efficiency and detection accuracy.Experiments on the TT100K traffic sign detection dataset show that,compared with the baseline algorithm,the proposed method improves precision and mean aver-age precision by 2.1%and 3%,respectively,while reducing the number of model parameters by 50%.On the CCTS-DB2021 traffic sign detection dataset,the proposed algorithm achieves improvements of 2.1%in precision and 1.3%in mean average precision over the baseline,demonstrating the effectiveness of the proposed approach.关键词
交通标志检测/轻量化/特征提取/多尺度特征融合/下采样Key words
traffic sign detection/lightweight/feature extraction/multi scale feature fusion/downsampling分类
信息技术与安全科学引用本文复制引用
李强,于金霞,朱明甫..多尺度融合的轻量化交通标志检测算法[J].重庆邮电大学学报（自然科学版）,2026,38(1):83-92,10.基金项目
河南省重点研发专项项目(231111210500) Key R&D Special Project of Henan Province(231111210500) （231111210500）
