融合注意力机制的YOLOv8-TS交通标志检测网络  

YOLOv8-TS traffic sign detection network integrating attention mechanism

作　　者：黄智渊 方遒 郭星浩 HUANG Zhiyuan;FANG Qiu;GUO Xinghao(School of Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024,China;School of Aerospace Engineering,Xiamen University,Xiamen 361005,China)

机构地区：[1]厦门理工学院机械与汽车工程学院,福建厦门361024 [2]厦门大学航空航天学院,福建厦门361005

出　　处：《现代电子技术》2025年第1期179-186,共8页Modern Electronics Technique

基　　金：福建省自然科学基金项目(2022J011247)。

摘　　要：道路交通标志识别是自动驾驶、车联网的重要组成部分,为进一步提高交通标志检测的精度和速度,提出一种基于YOLOv8s改进的YOLOv8-TS道路交通标志检测网络。首先,对YOLOv8s进行了整体的轻量化设计,并设计了Conv-G7S和CSP-G7S模块,减少了网络的参数量;其次,设计了CSP-SwinTransformer模块,强化了模型利用窗口内的特征信息进行上下文感知和建模的能力;然后,在颈部网络融合了卷积注意力机制(CBAM),强化了模型对不同通道、空间权重信息的学习;最后,对损失函数进行了改进,提升了边界框回归性能。实验结果表明,在中国道路交通标志TT100K数据集上,精确率(Precision)、平均精度(mAP@0.5)分别提高了6.9%、3.7%,而改进后模型的参数量下降了75.4%,模型的大小仅为5.8 MB,平均精度(mAP@0.5)达到96.5%,检测速度由126.58 f/s提升至136.99 f/s。Road traffic sign recognition is an important part of automatic driving and Internet of Vehicles(IoV).In view of this,the paper proposes an improved YOLOv8-TS road traffic sign detection network based on YOLOv8s to further improve the accuracy and speed of traffic sign detection.The overall lightweight design of the YOLOv8s is carried out.The Conv-G7S and CSP-G7S modules are designed to reduce the number of network parameters.The CSP-SwinTransformer block is designed to enhance the ability of the model to use the feature information in the window for context awareness and modeling.Then,CBAM(convolutional block attention module)is integrated in the neck network to strengthen the learning of different channels and spatial weight information.The loss function is improved to improve the performance of boundary box regression.The experimental results show that on the TT100K data set of Chinese road traffic signs,the precision and the mAP@0.5 are improved by 6.9%and 3.7%,respectively,while the parameters of the improved model decreases by 75.4%,its size is only 5.8 MB,its mAP@0.5 reaches 96.5%,and its detection speed is increased from 126.58 f/s to 136.99 f/s.

关 键 词：交通标志检测 YOLOv8-TS 轻量化 注意力机制 Conv-G7S WIoU 

分 类 号：TN911.73-34[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]