河北工业科技2026，Vol.43Issue(1)：10-20,91,12.DOI:10.7535/hbgykj.2026yx01002
基于多尺度特征的轻量级交通标志检测模型
Lightweight traffic sign detection model based on multi-scale features
摘要
Abstract
To address the challenges of balancing accuracy and speed,as well as the high missed detection rate of small targets in existing traffic sign detection methods,a lightweight real-time traffic sign detection model(LRTD)was proposed.Based on YOLO11n as the baseline,the model introduced a feature enhancement block(FEB)and a global-local collaborative attention module(GLCAM)into the backbone network;In the neck network,a multi-scale receptive field collaborative module(MSRFC)was designed and the feature fusion strategy was optimized to construct a high-resolution detection head.On the public datasets CCTSDB and GTSDB,performance comparisons between the LRTD model and state-of-the-art detection models were conducted,and ablation experiments were carried out to verify the functionality of each module.The results show that on the CCTSDB and GTSDB datasets,the LRTD model achieves mAP@50 of 83.1%and 95.6%respectively,and mAP@50-95 of 55.6%and 81.5%respectively.Compared with the YOLO11n model,it increases mAP@50 by 6.7 percentage points and 2.1 percentage points respectively,and mAP@50-95 by 6.0 percentage points and 4.5 percentage points respectively.Additionally,the model maintains a real-time inference speed of 155.0 fps on the CCTSDB dataset,with its parameter count and computational complexity reduced by 1.9 percentage points and 1.6 percentage points respectively.The proposed model can effectively improve the recognition performance of traffic signs in complex scenarios and provide a feasible technical solution for real-time object detection tasks in intelligent transportation systems.关键词
计算机图像处理/交通标志检测/特征增强/注意力机制/多尺度特征融合Key words
computer image processing/traffic sign detection/feature enhancement/attention mechanism/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
田泉泉,张杨..基于多尺度特征的轻量级交通标志检测模型[J].河北工业科技,2026,43(1):10-20,91,12.基金项目
河北省自然科学基金(F2023208001) （F2023208001）
河北省引进留学人员资助项目(C20230358) （C20230358）
