Authors:
Amir Ismail
1
;
2
;
Maroua Mehri
2
;
Anis Sahbani
1
and
Najoua Essoukri Ben Amara
2
Affiliations:
1
Enova Robotics, Novation City, Technopôle de Sousse, 4000, Sousse, Tunisia
;
2
Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia
Keyword(s):
Vehicle License Plate, Detection, Real-time, Video, Mobile Robot, Deep Neural Networks, YOLO Architectures.
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.
(More)