计算机工程2025，Vol.51Issue(7)：90-99,10.DOI:10.19678/j.issn.1000-3428.0069409
基于DMC-YOLO的交通标志实时检测算法
Real-time Traffic Sign Detection Algorithm Based on DMC-YOLO
摘要
Abstract
In traffic sign detection,external environmental interference and the small size of traffic sign targets in driving scenarios hinder detection performance.This paper introduces a novel traffic sign detection algorithm that significantly improves model detection precision while ensuring real-time detection capabilities.This paper initially designs a new multi-scale feature extraction network,incorporating large-scale features to augment small target localization information,and simultaneously designs a multi-scale feature attention enhancement module to further enhance the model's feature extraction capability.Second,to reduce the computational load and complexity of the model,this paper improves the multi-scale detection heads of the original model by selecting two large-scale detection heads for detecting small targets.Finally,the algorithm modifies the Complete Intersection over Union(CIoU)loss function to enhance its perception of small targets and improve the network's training efficiency.On two open-source public datasets,namely the TT100K and CCTSDB 2021 traffic sign datasets,the improved model achieves enhanced detection precision for small traffic sign targets,with a mean Average Precision(mAP)of 84.8％and 83.6％on the test sets,respectively.These results show improvements of 3.0 and 3.6 percentage points over the baseline models,respectively,demonstrating the model's higher detection performance and feature extraction capabilities while meeting the requirements for real-time detection.关键词
交通标志检测/多尺度特征融合/注意力机制/膨胀卷积/小目标检测Key words
traffic sign detection/multi-scale feature fusion/attention mechanism/dilated convolution/small object detection分类
信息技术与安全科学引用本文复制引用
栾孟娜,郑秋梅,王风华..基于DMC-YOLO的交通标志实时检测算法[J].计算机工程,2025,51(7):90-99,10.基金项目
国家自然科学基金(52074341,51874340) （52074341,51874340）
中央高校基本科研业务费专项资金(19CX02030A). （19CX02030A）
