DETEKSI PLAT NOMOR KENDARAAN MENGGUNAKAN METODE YOLOv8 - Putra, Lipi Amanda; Yohannes, Yohannes (Universitas Multi Data Palembang, 2025)
Motorized vehicles play a crucial role in daily life, making vehicle management and monitoring increasingly necessary. One common issue arises in parking systems, where current systems only capture photos of vehicles and still require manual input of license plate numbers upon vehicle exit. These systems are not yet capable of automatically detecting and recognizing license plates. Therefore, this study aims to design an application for license plate recognition using the YOLOv8 method to automatically and accurately detect license plates. YOLOv8 is a fast and accurate object detection model. The dataset used consists of 764 images of vehicle license plates, divided into 70% training data, 20% validation data, and 10% test data. he results of the study show a detection accuracy with a precision value of 94.3%, recall of 87.3%, and mAP of 95.3%.