Automatic License Plate Recognition System for Vehicles Using a CNN

Kumar, Yogesh; Pandian, S. Chenthur

doi:10.32604/cmc.2022.017681

Cited by 34 publications

(15 citation statements)

References 17 publications

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“…Avoiding the issue of long-term dependency is the main focus of the algorithm’s design. LSTMs are structured like chains; however, the repeating module is structured differently [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

Nagy

Aljabri

Shaahid

et al. 2023

Sensors

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In today’s digitalized era, the world wide web services are a vital aspect of each individual’s daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users’ personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

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“…Avoiding the issue of long-term dependency is the main focus of the algorithm’s design. LSTMs are structured like chains; however, the repeating module is structured differently [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

Nagy

Aljabri

Shaahid

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To find the best suitable Swin-cryoEM model for training cryo-electron micrographs images (α, β), select Beta Distribution as (5,1), (2,5),(1,3),(0.5,0.5),(2, 2) and other commonly used (α, β) parameters are trained in the Swin-cryoEM model for cryo-electron micrographs images. The specific experimental results are shown in Table 1.…”

Section: Incorporating Mixup Mixed Class Enhancement Algorithmmentioning

confidence: 99%

“…The Fast ParticlePicker [ 1 ] proposed in 2017 uses the Faster R-CNN [ 2 ] architecture with a simplified region proposal approach to automate human-level particle extraction tasks. However, it uses sliding windows to obtain regions of interest, and its performance mainly depends on the classification network.…”

Section: Introductionmentioning

confidence: 99%

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method

Fang,

Wang,

Chen

et al. 2024

PLoS ONE

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Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network’s tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.

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“…Deep learning works reasonably well in terms of object detection. Character segmentation and character recognition are two ALPR processes suggested [24,25].…”

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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Automatic License Plate Recognition System for Vehicles Using a CNN

Cited by 34 publications

References 17 publications

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method

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

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