Sistema de control de acceso vehicular mediante reconocimiento de placas para el personal de la Pontificia Universidad Católica del Ecuador Sede Ibarra - Mejía Sandoval, Dany René (2025)
This report details the development of a prototype for a vehicle access control system based on automatic license plate recognition using computer vision and artificial intelligence. The system captures vehicle images through a security camera, processes them with OpenCV for plate detection and segmentation, and utilizes EasyOCR and TensorFlow for character extraction and recognition. Once the license plate is identified, the information is compared with a database stored in MySQL, where authorized vehicles and access events are recorded. For real-time monitoring and data management, a web interface developed with Quasar Framework was implemented, allowing users to remotely supervise vehicle access from any network connected device. The system also integrates an ESP32 microcontroller and a relay module, enabling the automation of a barrier or electric gate, which is activated only when an authorized license plate is recognized. This combination of computer vision, deep learning with TensorFlow, and IoT provides an efficient and cost-effective solution to enhance security in parking lots and restricted areas. Tests conducted with the prototype demonstrate that the system is functional and capable of recognizing license plates under different lighting conditions and viewing angles. These results allow for performance evaluation and potential improvements in future versions to increase accuracy and adaptability in real-world environments.