With the rapid development of intelligent driving technology, in order to meet the high-precision positioning requirements of intelligent vehicles, this paper considers the problem that high-speed moving vehicle mounted cameras can easily produce blurry or blurred images, leading to difficulties in extracting QR code information and affecting recognition accuracy. Therefore, a QR road sign detection and recognition technology based on Yolov5 and GAN is proposed. Firstly, the object detection method based on deep learning YOLOv5 is used to detect and locate QR code images; Then, GAN is used to enhance the target image, generate clear QR codes, and finally decode the QR image to obtain information and obtain the QR code information. By identifying five indicator parameters including target quantity, detection rate, effective recognition distance, recognition rate, and detection time, and comparing them with open-source tools such as Pyzbar and WeChat scanning engine. Experiments have shown that during high-speed driving, the detection and recognition rates of this method are both over 95%. It has similar detection and recognition rates to the other two mature methods, but the number of targets detected by this method in the same road environment is about twice that of other methods, and the effective recognition distance is longer. This indicates that this method helps high-speed vehicles obtain more roadside QR code information and can obtain higher accuracy vehicle positioning information. Therefore, this method can effectively meet the high-precision requirements of vehicles under high-speed operating conditions while meeting the detection and recognition rates. It is also suitable for vehicle positioning needs in scenarios such as tunnels where GPS signals are interfered with or lost.