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.