A Real-Time License Plate Detection Method Using a Deep Learning Approach

Khazaee, Saeed; Tourani, Ali; Soroori, Sajjad; Shahbahrami, Asadollah; Suen, Ching Y.

doi:10.1007/978-3-030-59830-3_37

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Another example is the trust environment provided by [26] based on an intelligent vehicle architecture. The three main parts of the system are a communication network, blockchain technology, and mobile cloud computing.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

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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. KEYWORDS Intelligent transportation system; blockchain technology; license plate recognition; privacy; YOLO; deep learning technique; ALPRAdditionally, noise removal techniques such as Gaussian blur and median filtering were applied to remove any noise or artifacts in the license plate images. Finally, the pre-processed dataset was split into training, and testing sets with a ratio of 80:20 to evaluate the model's performance on unseen data. These pre-processing techniques ensure that the license plate recognition model is trained on a high-quality dataset, leading to improved accuracy in the recognition process. Materials and MethodsInformation about traffic management, environmental changes, and congestion is gathered and disseminated through a transportation system known as ITS. Communications between vehicles and between vehicles and infrastructure are made possible by ITS in various ways. In addition, ITS includes information from both wired and wireless connections. Infrastructure-to-infrastructure communication typically uses cable technology, while V2V and V2I communication frequently use wireles...

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Section: Literature Reviewmentioning

confidence: 99%

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

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…Since licence plate detection should also be a real-time process, YOLO models are again taken into consideration. [14] uses YOLOv3 to detect the license plate without initially detecting the vehicle, training on a dataset in which license plates are shot in a relatively clear, controlled and ideal environment, and achieve an accuracy of 97.9% and a recall of 97.2%.…”

Section: License Plate Detectionmentioning

confidence: 99%

Measurement of distance between vehicles based on monocular vision

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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This paper proposes a method to calculate the distance between vehicles from the perspective of the car, in which monocular vision information from the driving recorders of the host car is used to calculate the distance between the host car and the car ahead. The author first detects all the vehicles with their type in an image input, sifts out the front vehicle from them, and locates the license plate from the bounding box of the vehicle. Then the paper designs an algorithm to measure the length of the license plate in the bounding box of the license plate and utilizes the common characteristic of license plates in America and in other countries that all the plates of one type of vehicles in one country share to derive a novel method to convert the length of the plate in the image into distance between the driving recorder and the license plate, therefore obtaining car-to-car distance.

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“…The authors of this paper have contributed to many computer vision and machine learning projects and proposed various approaches in the field of ITS. Some of these approaches include vehicle count using video processing [16], deep learning-based vehicle detection [17], vehicle speed measurement [18][19], license plate localization [8,20], and Farsi character recognition [8]. Accordingly, we claim that we have felt the essence of reliable data for the development of domestic robust applications for Fig.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

Tourani

Soroori

Shahbahrami

et al. 2021

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Providing huge amounts of data is a fundamental demand when dealing with Deep Neural Networks (DNNs). Employing these algorithms to solve computer vision problems resulted in the advent of various image datasets to feed the most common visual imagery deep structures, known as Convolutional Neural Networks (CNNs). In this regard, some datasets can be found that contain hundreds or even thousands of images for license plate detection and optical character recognition purposes. However, no publicly available image dataset provides such data for the recognition of Farsi characters used in car license plates. The gap has to be filled due to the numerous advantages of developing accurate deep learning-based systems for law enforcement and surveillance purposes. This paper introduces a large-scale dataset that includes images of numbers and characters used in Iranian car license plates. The dataset, named Iranis, contains more than 83,000 images of Farsi numbers and letters collected from real-world license plate images captured by various cameras. The variety of instances in terms of camera shooting angle, illumination, resolution, and contrast make the dataset a proper choice for training DNNs. Dataset images are manually annotated for object detection and image classification. Finally, and to build a baseline for Farsi character recognition, the paper provides a performance analysis using a YOLO v.3 object detector.

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A Real-Time License Plate Detection Method Using a Deep Learning Approach

Cited by 7 publications

References 27 publications

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

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

Measurement of distance between vehicles based on monocular vision

Iranis: A Large-scale Dataset of Iranian Vehicles License Plate Characters

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