With the rapid development of science and technology and artificial intelligence， people are more and more inclined to driverless technology. Considering the safety problem， aiming at the real-time detection of traffic signs during driving， the algorithm is improved on the basis of YOLOv5 model， and a lightweight traffic sign detection algorithm is proposed. The attention mechanism is added to the feature fusion part of the model， which can make the model more prominent target features. Then a lightweight sub-pixel convolution layer is added in front of the detection layer to effectively improve the resolution of the detection feature map without increasing the amount of computation. Finally， the loss function CIoU （Complete intersection over union） is improved， which speeds up the convergence speed of the network， and the convergence effect is better than that before the improvement. The experimental results show that the accuracy of this model reaches 90.6%， which is 14.5% higher than the basic network， and the detection speed reaches 70 frames / s， which basically meets the real-time accurate detection of traffic signs.