Vehicle and label detection method based on YOLOv11

This paper proposes a vehicle and label detection method based on the YOLOv11 algorithm. By constructing a custom dataset of vehicles and labels, the YOLOv11 model is trained to achieve precise detection of vehicles and their rear-mounted labels. This paper details the dataset creation process, label design, model training procedure, and the selection of optimal training parameters. Finally, the algorithm's performance is evaluated using a validation set. Experimental results demonstrate that the YOLOv11-based vehicle and label detection method exhibits strong performance in terms of accuracy and real-time capability, meeting the requirements of practical applications.