License Plate Number Detection in Drone Images

Abstract

This work aims to figure out a way to accurately identify license plate numbers in photos taken by drones. This technology is used in practical applications like managing parking and traffic. The goal is to extract features from the images that are robust and invariant features using the phase congruency model. These proposed features can handle the challenges posed by drone images. After that, the work will take advantage of a fully connected neural network to tackle the difficulties of fixing precise bounding boxes regardless of orientations, shapes, and text sizes. The proposed work will be able to find the detected text for both license plate numbers and natural scene images which will lead to a better recognition stage. Both our drone dataset and the benchmark license plate dataset (Medialab) are used to assess the effectiveness of the study that has been done. To show that the suggested system can detect text of natural scenes in a wide variety of situations. Four benchmark datasets, namely SVT, MSRA-TD-500, ICDAR 2017 MLT, and Total-Text are used for the experimental results. We also describe trials that demonstrate robustness to varying height distances and angles. The code and data used in the study will be made available on GitHub.

Received: 23 September 2022 | Revised: 10 November 2022 | Accepted: 11 November 2022

Conflicts of Interest

Palaiahnakote Shivakumara is the Editor-in-Chief for Artificial Intelligence and Applications, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in https://github.com/Amirhossein-Nayebi/Nordic-Vehicle-Dataset at https://nvd.ltu-ai.dev/.

Author Contribution Statement

Hamam Mokayed: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision. Palaiahnakote Shivakumara: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision. Lama Alkhaled: Methodology, Software, Validation, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Ahmed N. AL-Masri: Methodology, Validation, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization.