黑龙江科技大学学报2025，Vol.35Issue(2)：344-348,5.DOI:10.3969/j.issn.2095-7262.2025.02.026
改进YOLOv8的交通标志检测模型
Improved YOLOv8 model for traffic sign detection
摘要
Abstract
This paper is intended to address the low detection accuracy of the traffic signs in complex backgrounds,the missed and fault detections of small-target traffic signs,and the hard deployment due to oversized model,and proposes an improved traffic sign detection model based on YOLOv8.The study consists of designing a lightweight C2fRVB module to replace the original C2f module,enhancing the global feature extraction capabilities,reducing the parameters by RepViTBlock,introducing a small-target detection layer,and integrating shallow feature details to improve recognition of small targets;and adop-ting ADown downsampling instead of traditional convolutional downsampling to minimize the loss of fea-ture map information,and boost the detection accuracy.The results demonstrate that the improved model enables the precision,recall,and average accuracy of 78.8%,69.2%,and 77.1%respectively in the TT100K dataset,representing the improvements of 7.9%,5.5%,and 7.2%over against the YOLOv8n model,while reducing parameters by 38.9%.The optimized model achieves both lightweight design and better recognition accuracy.关键词
YOLOv8/交通标志/RepViT/小目标检测层/下采样Key words
YOLOv8/traffic signs/RepViT/small target detection layer/down-sampling分类
信息技术与安全科学引用本文复制引用
赵艳芹,赵文栋..改进YOLOv8的交通标志检测模型[J].黑龙江科技大学学报,2025,35(2):344-348,5.基金项目
黑龙江省省属高等学校基本科研业务费项目(2022-KYYWF-0565) （2022-KYYWF-0565）
