YOLOV12 Based on Stationary Vehicle for License Plate Detection - Kurniawan, The, Obed Danny; Rachmawanto, Eko Hari (Journal of Applied Informatics and Computing, 2025)
The use of technology for vehicle license plate recognition in this modern era is increasingly developing in supporting the needs of more effective transportation system management. This research aims to design and implement a vehicle license plate recognition system with the YOLOv12 (You Only Look Once) algorithm. The use of the YOLOv12 algorithm in license plate recognition is due to its superiority in detecting and recognizing objects in real-time with high accuracy. This research method will involve collecting a dataset of vehicle license plates from various viewing angles, lighting conditions, license plate colors, and the shape of the license plate. These datasets are then used to train an adapted YOLOv12 model to detect and recognize characters on license plates. Tests are conducted by measuring the detection accuracy, processing speed, and robustness of the detection system to disturbances such as noise and variations in environmental conditions when detecting license plates. The results of the study shown that this system yielded accuracy rate of 97.5%, recall of 95.4%, precision of 96.7%, and is capable of recognizing characters on vehicle license plates with an accuracy rate of 88%, recall of 87%, and precision of 85.8%. The average processing time is 1 second per image on CPU and 20 seconds per image on GPU. The system's ability to detect vehicle license plates shows that the YOLOv12 algorithm can be used for large-scale vehicle license plate system implementation. The significance of these results lies in their potential application in various fields such as parking management systems, traffic management, and law enforcement, which can improve efficiency and safety.