Performance Benchmarking of YOLO Architectures for Vehicle License Plate Detection from Real-time Videos Captured by a Mobile Robot

Amir Ismail, Amir Ismail, Maroua Mehri, Anis Sahbani, Najoua Essoukri Ben Amara

2021

Abstract

In this paper, we address the issue of vehicle license plate (LP) detection for a mobile robotic application. Specifically, we tackle the dynamic scenario of a robot in the physical world interacting based on its cameras. The robot is dedicated essentially to patrol and secure unconstrained environments. Counter to the most recent works of LP detection which assume controlled deploying scenario, the mobile platform requires a more robust system that is suitable for various complex scenarios. To contribute to this purpose, we propose an end-to-end detection module capable of localizing LP either in images or in live-streaming videos. The proposed system is based on deep learning based detectors, particularly the most recent YOLOv4-tiny one. To evaluate the proposed system, we introduce the first-ever public Tunisian dataset, called PGTLP, for LP detection that contains 3,000 annotated images. This dataset was gathered using the security robot during its patrolling and surveillance of parking stations and high-risk areas. For the detection, a comparative study for the different YOLO variants has been carried out in order to select the best detector. Our experiments are performed on the PGTLP images and following the same experimental protocol. Among the selected models, YOLOv4-tiny reveals the best compromise between detection performance and complexity. Further experiments that have been conducted using the AOLP benchmark dataset point out that the proposed system has satisfying results.

Paper Citation

in Harvard Style

Ismail A., Mehri M., Sahbani A. and Ben Amara N. (2021). Performance Benchmarking of YOLO Architectures for Vehicle License Plate Detection from Real-time Videos Captured by a Mobile Robot. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 661-668. DOI: 10.5220/0010349106610668

in Bibtex Style

@conference{visapp21,
author={Amir Ismail and Maroua Mehri and Anis Sahbani and Najoua Essoukri Ben Amara},
title={Performance Benchmarking of YOLO Architectures for Vehicle License Plate Detection from Real-time Videos Captured by a Mobile Robot},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={661-668},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010349106610668},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Performance Benchmarking of YOLO Architectures for Vehicle License Plate Detection from Real-time Videos Captured by a Mobile Robot
SN - 978-989-758-488-6
AU - Ismail A.
AU - Mehri M.
AU - Sahbani A.
AU - Ben Amara N.
PY - 2021
SP - 661
EP - 668
DO - 10.5220/0010349106610668
PB - SciTePress