PlateGuard: Ensuring Security with YOLOv5 ANPR Technology

Abstract

The accomplishment of an Automatic Number Plate Recognition (ANPR) system stands as a pivotal solution in fortifying security measures for compounds necessitating stringent access control, such as educational institutions. In this pursuit, the focus lies on designing an efficacious ANPR system tailored to ensure only authorized vehicles gain entry into campus premises. Utilizing the advanced functionalities of the YOLOv5 (You Only Look Once) algorithm, celebrated for its instantaneous object detection abilities, the system excels in promptly recognizing vehicles as they approach the specified entry points. Upon detection, it promptly captures comprehensive vehicle images to initiate subsequent processing stages. Extracting the vehicle’s number plate becomes paramount, followed by cross-referencing it against a meticulously curated registry of authorized faculty members’ vehicles. Should a match occur, access is seamlessly granted; contrarily,unauthorized vehicles elicit a distinctive alert, signalling denial of entry. Implemented using Python, the system’s performance undergoes rigorous evaluation using authentic real-world images sourced from campus gates.