Résumé:
This study presents the development of an access control system for highway tolls using Automatic License Plate Recognition (ALPR) integrated with YOLOv8 for plate detection and a PHP platform for driver management. The goal is to automate vehicle identification and access management at toll booths, thereby improving efficiency and security. The system architecture includes a YOLOv8 model trained for real-time plate detection and a PHP platform for managing driver and vehicle data. The PHP platform provides an interface for driver registration and manages vehicle access based on recognized plates, thereby controlling the toll barriers. Data on vehicle entry and exit are recorded and processed to effectively manage toll barriers. Key components of the study include: Dataset Preparation: Collecting and annotating diverse images of license plates to train the YOLOv8 model. Model Training: Training and optimizing the YOLOv8 model to achieve accurate and fast plate recognition. PHP Platform Development: Creating a PHP application for managing driver and vehicle data, integrated with YOLOv8 detection results for access control management. System Integration: Implementing a module to control cameras and toll barriers based on recognition results, ensuring dynamic system response to real-time vehicle detection. The results demonstrate that the YOLOv8 model provides accurate and efficient license plate recognition, while the PHP platform enables effective vehicle access management. The system automates toll operations, reduces waiting times, and enhances security, showing potential for large-scale deployment in real-world highway toll contexts.
