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Updated: May 14, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Automated vehicle access control system using license plate recognition.

R Advaith1, Venkat K Uditya1, S Hariharan1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.

Scientific Reports
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Automated Vehicle Access Control system using deep learning-based license plate recognition. The smart parking solution effectively manages university parking demand, reducing congestion and operational costs.

Keywords:
Automated control systemLicence plate recognitionMachine learningParking

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Last Updated: May 14, 2026

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08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Smart Transportation

Background:

  • Increasing university parking demand necessitates advanced solutions.
  • Traditional parking management faces challenges with congestion and space optimization.
  • Smart parking systems offer a viable approach to mitigate these issues.

Purpose of the Study:

  • To develop and evaluate an Automated Vehicle Access Control (AVAC) system.
  • To leverage deep learning for efficient license plate recognition (LPR) in smart parking.
  • To address parking challenges, reduce congestion, and optimize space utilization.

Main Methods:

  • Implemented a deep learning model for vehicle license plate detection using InceptionResNetV2.
  • Utilized a hybrid MobileNetV2-EfficientNetV2 architecture for license plate character recognition.
  • Trained the AVAC system on a large Indian vehicle dataset (3000+ images/character class).

Main Results:

  • Achieved high performance in plate detection with an IoU of 0.815 and F1 score of 0.973.
  • Reached 98.59% accuracy with a 1.41% Character Error Rate (CER) for character recognition.
  • Demonstrated real-time access control with a mean inference time of <50 ms on NVIDIA T4 GPUs.

Conclusions:

  • The developed AVAC system provides effective real-time vehicle access control.
  • The system streamlines authentication, parking assignment, and access management.
  • AVAC contributes to minimizing traffic congestion and reducing operational costs in smart parking environments.