基于YOLOv5的交通标志识别  

Traffic Sign Recognition Based on YOLOv5

作　　者：郑红彬 宋晓茹[1] 刘康 ZHENG Hong-Bin;SONG Xiao-Ru;LIU Kang(School of Electronic Information Engineering,Xi’an Technological University,Xi’an 710021,China)

机构地区：[1]西安工业大学电子信息工程学院,西安710021

出　　处：《计算机系统应用》2023年第8期230-237,共8页Computer Systems & Applications

基　　金：陕西省重点研发计划(2021GY-287)。

摘　　要：交通标志识别是自动驾驶技术中的关键一部分.针对交通标志在道路场景中目标较小且识别精度较低的问题,提出一种改进的YOLOv5算法.首先在YOLOv5模型中引入全局注意力机制(GAM),提高网络捕获不同尺度交通标志特征的能力;其次将YOLOv5算法中使用的GIoU损失函数更换为更具回归特性的CIoU损失函数来优化模型,提高对交通标志的识别精度.最后在Tsinghua-Tencent 100K数据集上进行训练,实验结果表明,改进后的YOLOv5算法对交通标志识别的平均精度均值为93.00%,相比于原算法提升了5.72%,具有更好的识别性能.Traffic sign recognition is a key part of autonomous driving technology.Given the problems of small targets and low recognition accuracy of traffic signs in road scenes,an improved YOLOv5 algorithm is proposed.First,the global attention mechanism(GAM)is introduced into the YOLOv5 model to improve the network’s ability to capture traffic sign features of different scales.Second,the GIoU loss function used in the YOLOv5 algorithm is replaced with the CIoU loss function which is more regressive to optimize the model and improve the recognition accuracy of traffic signs.Finally,the training is carried out on the Tsinghua-Tencent 100K dataset.The experimental results show that the average accuracy of the improved YOLOv5 algorithm for traffic sign recognition is 93.00%,which is 5.72%higher than that of the original one,indicating that the improved algorithm has better recognition performance.

关 键 词：YOLOv5 交通标志识别 GAM CIoU 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U463.6[自动化与计算机技术—计算机科学与技术]