This study aims to detect traffic signs accurately and in real time, reduce traffic accidents, and promote intelligent transportation. An improved YOLOv5s detection algorithm, YOLOv5s-GC, based on computer vision technology is designed to solve the problems of insufficient accuracy, large weight files, and slow detection speed of existing detection models for road traffic signs. Firstly, data is enhanced by copy-paste and then sent to the network for training to improve the detection ability of small targets. Then, Ghost is introduced to build the network, reducing the parameters and calculation amount of the original network, and realizing a lightweight network. Finally, the coordinate attention mechanism is added to the backbone network to enhance the representation and positioning of the attention target and improve detection accuracy. The experimental results show that in comparison with the YOLOv5s, the number of parameters of the YOLOv5s-GC network model is reduced by 12%; the detection speed is increased by 22%; the average accuracy reaches 94.2%. The YOLOv5s-GC model is easy to deploy and can meet the speed and accuracy requirements of traffic sign recognition in actual autonomous driving scenarios.