License Plate Detection: A YOLO-Based Approach for Traffic and Security Applications

The increasing adoption of automated systems for traffic management, surveillance, and security highlights the necessity of efficient license plate detection. Manual methods for extracting and storing license plate information are time-consuming and error-prone. This paper presents the design and implementation of a system for license plate detection, extraction, and integration with a database for data storage and management. The system leverages advanced computer vision techniques, including YOLO (You Only Look Once), for real-time, accurate detection of license plates from images or video streams. It incorporates Python for backend processing, SQLite for data management, and Tkinter for the graphical user interface. The proposed system is scalable, adaptable to diverse applications, and aims to improve operational efficiency in vehicle-related data management. The results demonstrate high accuracy and reliability, making it a valuable tool for modern transportation and security systems.