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.
万方数据
维普
