Smart garage access control system to improve the license plate recognition process

Resumen

The aim of this investigation is to develop a smart garage access control system to improve the vehicle license plate recognition process, optimizing accuracy and response times in the identification and registration processes. Many current systems rely on manual intervention, causing delays and inconveniences. This system is based on the automated identification and verification of vehicle license plates, ensuring precise and efficient recognition. To achieve accurate recognition, YOLO neural networks, Python image processing techniques, and Optical Character Recognition (OCR) were used to extract data from the captured plates. The IP camera captured the vehicle plates, and access was validated by comparing the data with a database of authorized residents. These technologies enabled license plate detection in real-time, even under various environmental conditions, ensuring high accuracy. The system records and monitors activities in real-time, providing valuable data on the performance of license plate recognition. The user interface displayed images and analysis results, allowing for automatic garage door opening or manual intervention in case of error. This ensured more agile and efficient processes compared to traditional manual methods.