Automatic Number Plate Recognition System Using Deep Learning YOLOv5 Algorithm

A computer vision system called Automatic Number Plate Recognition (ANPR) automates the process of reading licence plates from moving objects. Because to its capacity for precise and effective object recognition, deep learning-based object detection algorithms like YOLO (You Only Look Once) have recently demonstrated promising results in ANPR systems. This study suggests a YOLOv5 object detection model-based architecture for an ANPR system. The suggested architecture consists of a preprocessing stage for image enhancement, a YOLOv5 detector for licence plate detection, post-processing methods like NMS to fine-tune the detection results, an Optical Character Recognition (OCR) algorithm to extract characters from the licence plate, and a decoding stage to ascertain the actual plate number. Real-world datasets are used to assess the proposed design. and displays encouraging accuracy and productivity outcomes. The potential uses for the proposed ANPR system include traffic monitoring, parking management, and law enforcement.