电子科技2026，Vol.39Issue(4)：8-18,11.DOI:10.16180/j.cnki.issn1007-7820.2026.04.002
复杂场景下的道路交通标志识别研究
Research on Road Traffic Sign Recognition in Complex Scenario
摘要
Abstract
In view of the problems of low recognition accuracy and high missed detection rate of traffic signs un-der complex environmental conditions,an improved traffic sign recognition algorithm YOLO-Traffic based on YOLOv8(You Only Look Once version 8)is proposed.The multi-scale information extraction ability of the network is enhanced through scale sequence feature fusion and triple feature coding.The local fine-grained features of traffic signs are fully extracted by adding a small target detection layer and refining the local feature mapping.The CA(Co-ordinate Attention)attention mechanism is introduced into the backbone network to enhance the model's ability to fo-cus on key regions.The new metric NWD(Normalized Wasserstein Distance)is adopted to replace the CIoU(Com-plete Intersection over Union)in the regression loss function of the detection head,strengthening the detection ability for small targets.The experimental results show that the mAP@0.5(mean Average Precision)of the original model is 90.4%,the mAP@0.5:0.95 is 63.2%,and the model size is 6.3 MB.The mAP@0.5 of the improved model is 95.5%,the mAP@0.5:0.95 is 67.5%,and the model size is 5.2 MB.Compared with the original model,the vol-ume of the improved model is reduced by 17.5%.The improved algorithm reduces the volume of model parameters while enhancing detection accuracy,and can meet the requirements of various complex road conditions and light-weight in practical application scenarios.关键词
小目标检测/交通标志/YOLOv8/轻量化网络/注意力机制/特征融合/损失函数/深度学习Key words
small object detection/traffic sign/YOLOv8/lghtweight network/atention mechanism/feature fu-sion/loss function/deep learning分类
信息技术与安全科学引用本文复制引用
何骞炜,张轩雄..复杂场景下的道路交通标志识别研究[J].电子科技,2026,39(4):8-18,11.基金项目
国家自然科学基金(62276167)National Natural Science Foundation of China(62276167) （62276167）
