Real-Time Vehicle Sticker Recognition for Smart Gate Control with YOLOv8 and Raspberry Pi 4

In today’s fast-paced world, secure and efficient access control is crucial for places like schools, gated communities, and corporate campuses. The system must overcome the issues of manual checking and record maintenance of traditional methods like RFID cards or license plate recognition. Our work introduces a budget-friendly, automated solution. A prototype was developed for a vehicle sticker recognition system to control and monitor gate access at NFC IET University as a case study. The automated system design will replace manual checking by detecting the car stickers issued to each vehicle by the university administration. An optimized lightweight YOLOv8 model is trained to identify three categories: IET stickers (authorized for access), non-IET stickers (unauthorized), and no sticker (denied access). A webcam connected to the Raspberry Pi 4 scans approaching vehicles. Authorized vehicles are allowed when the relevant class is detected, which signals a servo motor to open the gate. Otherwise, access to the gate is denied, and infrared (IR) sensors close the gates. A second set of IR sensors and a servo motor was also added to manage the exit side, preventing unauthorized tailgating. The system’s modular design makes it adaptable for different environments, and its use of affordable hardware and open-source tools keeps costs low, which is ideal for smaller institutions or communities. The prototype model is tested and trained on self-collected datasets comprising 506 images.

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Publication Details

Journal
Automation
Published
2025-10-29
DOI
https://doi.org/10.3390/automation6040063
Citations
1
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
1.14
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article

Real-Time Vehicle Sticker Recognition for Smart Gate Control with YOLOv8 and Raspberry Pi 4

Serosh Karim Noon, Ali Noor, Abdul Mannan, Muhammad Abdullah et al.
1 citations
Automation
Vehicle License Plate Recognition
1.14
article

Real-Time Vehicle Sticker Recognition for Smart Gate Control with YOLOv8 and Raspberry Pi 4

Serosh Karim Noon, Ali Noor, Abdul Mannan, Muhammad Abdullah, Mohd Fadzil Arshad, Turab Haider
article en
1 citations

Abstract

In today’s fast-paced world, secure and efficient access control is crucial for places like schools, gated communities, and corporate campuses. The system must overcome the issues of manual checking and record maintenance of traditional methods like RFID cards or license plate recognition. Our work introduces a budget-friendly, automated solution. A prototype was developed for a vehicle sticker recognition system to control and monitor gate access at NFC IET University as a case study. The automated system design will replace manual checking by detecting the car stickers issued to each vehicle by the university administration. An optimized lightweight YOLOv8 model is trained to identify three categories: IET stickers (authorized for access), non-IET stickers (unauthorized), and no sticker (denied access). A webcam connected to the Raspberry Pi 4 scans approaching vehicles. Authorized vehicles are allowed when the relevant class is detected, which signals a servo motor to open the gate. Otherwise, access to the gate is denied, and infrared (IR) sensors close the gates. A second set of IR sensors and a servo motor was also added to manage the exit side, preventing unauthorized tailgating. The system’s modular design makes it adaptable for different environments, and its use of affordable hardware and open-source tools keeps costs low, which is ideal for smaller institutions or communities. The prototype model is tested and trained on self-collected datasets comprising 506 images.

AutomationVol. 6(4)
Muhammad Nawaz Sharif University of Engineering & Technology (PK), NFC Institute of Engineering and Technology
Openalex Percentile: Top 15%
Vehicle License Plate Recognition
1.14
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Real-Time Vehicle Sticker Recognition for Smart Gate Control with YOLOv8 and Raspberry Pi 4 — Serosh Karim Noon, Ali Noor, et al. · Automation (2025) | TGRS Research Map | TGRS