In the face of complex road traffic scenes, intelligent, fast and accurate detection of road identifiers is of great significance to automatic driving technology. The YOLOv7 algorithm with fast detection speed and high accuracy is suitable for real-time complex road identifier detection. In this paper, the YOLOv7 model is trained with the Chinese traffic sign detection dataset, and three different road traffic scene images, namely, ordinary, occluded and blurred, are selected to test the training model and compared and analyzed with three popular target detection algorithms, namely, CenterNet, Faster R-CNN and SSD. The results show that the YOLOv7 algorithm is fast in detection, has the highest average accuracy with 89.7% mAP, and has the best performance in the image tests of the three scenarios, successfully detecting road marker targets in the images even in the presence of occlusion and blurring.