Open Access

ARTICLE

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Fares Alharbi¹, Reem Alshahrani², Mohammed Zakariah^3,*, Amjad Aldweesh¹, Abdulrahman Abdullah Alghamdi¹

1 Department of Computer Science, College of Computing and IT, Shaqra University, Shaqra, 15526, Saudi Arabia
2 Department of Computer Science, College of Computers and IT, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia
3 College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia

* Corresponding Author: Mohammed Zakariah. Email:

(This article belongs to the Special Issue: Innovations in Pervasive Computing and Communication Technologies)

Computers, Materials & Continua 2023, 77(3), 3697-3722. https://doi.org/10.32604/cmc.2023.040086

Received 04 March 2023; Accepted 28 June 2023; Issue published 26 December 2023

Abstract

Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing the code, there are still many challenges to consider when designing blockchain designs for the IoT environment. This study uses traditional blockchain technology with the “You Only Look Once” (YOLO) object detection method to accurately locate and identify license plates. While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising, they have received limited research attention. Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network (RCNN) built with the tensor flow core open source libraries. This method works reasonably well for identifying any license plate. The Automatic License Plate Recognition (ALPR) approach delivered outstanding results in various datasets. First, with a recognition rate of 96.2%, our system (UFPR-ALPR) surpassed the previously used technology, consisting of 4500 frames and around 150 films. Second, a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset. Third, the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly. The proposed model, however, produced beneficial outcomes.

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Keywords

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