Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases

Tang, Junqing; Wan, Li; Schooling, Jennifer; Zhao, Pengjun; Wei, Shufen

doi:10.1016/j.cities.2022.103833

Cited by 37 publications

(8 citation statements)

References 84 publications

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“…In many applications, such as automatic number plate recognition (ANPR) [21], vehicle monitoring, and traffic control systems, license plate detection is essential. In this field, numerous initiatives and research publications have been created.…”

Section: Related Workmentioning

confidence: 99%

License plate text recognition using deep learning, NLP, and image processing techniques

Moussaoui,

El Akkad,

Benslimane

2024

Stat., optim. inf. comput.

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Detecting license plates has never been easy, particularly with the proliferation of sophisticated radars on highways and roads. By 2021, the gendarmerie and National Security Road control agents will have access to more than 1 billion smart traffic radars worldwide. This research presents a revolutionary technique for detecting and recognizing Arabic and Latin license plates. After assembling the gathered images to create a novel dataset, we utilized YOLO v7 to locate and identify the number plate in the image as the first step of the suggested procedure. Before the dataset was fed to the detection system, it was manually labeled. Afterward, we improved the recognized license plate using machine learning methods. To do this, we used kernel methods as well as thresholding to get rid of the extra vertical lines on the plate. After that, we employed Arabic OCR along with Easy OCR methods to decipher the Latin and Arabic characters on the number plate. Eventually, the proposed method achieved an F1 score of 98%,with a precision and recall of 97% and 98%, respectively. We also obtained an accuracy of 99% for image segmentation. The segmentation and detection results from the suggested strategy have shown satisfactory results.

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Section: Related Workmentioning

confidence: 99%

License plate text recognition using deep learning, NLP, and image processing techniques

Moussaoui,

El Akkad,

Benslimane

2024

Stat., optim. inf. comput.

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“…Recent years have witnessed significant advancements in intelligent traffic monitoring technologies, driven by the rapid progress in computer vision, machine learning, and data analytics [6], [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in realworld scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

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“…Automated steps, such as capturing images, recognition of license plates, recording speeds, and packaging and transmitting communication modules using core HD camera equipment at traffic checkpoints, generate a huge unstructured data set (vehicle license plate data, henceforth referred to as VLPD) containing vehicle driving status and image information on a terminal server [8][9][10]. Since the VLPD includes rich information such as license plate numbers, the time, license plate colors, vehicle types, lanes, vehicle speeds, driving directions, and captured images, it is widely used in many applications such as traffic flow analysis, road planning, vehicle tracking, and security management [11][12][13][14]. The volume and availability of VLPD in ITS leads to the need for data-driven approaches.…”

Section: Introductionmentioning

confidence: 99%

A vehicle license plate data access model based on the jump hash consistency algorithm

Wang,

Cheng,

Chen

et al. 2023

PLoS ONE

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The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key–value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

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Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases

Cited by 37 publications

References 84 publications

License plate text recognition using deep learning, NLP, and image processing techniques

License plate text recognition using deep learning, NLP, and image processing techniques

A Deep Learning-based Framework for Vehicle License Plate Detection

A vehicle license plate data access model based on the jump hash consistency algorithm

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