A gate control system using ANPR (Automatic Number Plate Recognition) is very popular, as many companies offer various gate control options. Typically, the ANPR-based gate control system captures images of license plates with cameras and converts the images into characters using OCR (Optical Character Recognition). Then, the extracted number is checked against a database; if it matches, the gate opens; if not, it stays closed. In this paper, we develop a machine learning-based gate control system using ANPR. First, the system captures images of approaching vehicles with a camera. Next, the YOLOv8 algorithm is used to detect license plates and vehicles. Then, a license plate image is extracted and converted to text with OCR. The vehicle number is compared to the stored number in a database. Finally, the gate opens if the vehicle number matches; otherwise, it remains closed. Our machine learning-based gate control system demonstrates high accuracy and effectiveness in detecting license plates and vehicles. It has been thoroughly tested, with 2,395 detections in total, of which 2,370 were correct and 48 were incorrect, achieving an accuracy of 97.99%.

gate automation, ANPR, computer vision, machine learning, YOLOv8