Intelligent Automatic Gate Control System Based on Deep Learning

The necessity for smooth, safe, and effective access control gave rise to the idea of an intelligent automatic gate control system. Conventional gate control systems have frequently been linked to labor-intensive procedures that require physical keys or security officers to be authorized. But as technology continues to change our environment, there is an increasing need for integrated solutions that combine automation that is easy to use with security. Consequently, a deep learning-based intelligent automatic gate control system was provided in this study. In order to process each frame separately, the system first extracts a series of frames from the input video. Then, for each frame, a convolutional neural network (CNN) model is used to detect license plates. The YOLOv8 model is used to develop the proposed system. Next, the system proceeds license plate detection and recognition. The accuracy of the model reached a high level for the license plate detection and recognition tasks, with mAP50 values of 0.964 and 0.924, respectively. The experimental results demonstrate the effectiveness of the proposed system.