European Union Dataset and Annotation Tool for Real Time Automatic License Plate Detection and Blurring

Chan, Lap Yan; Zimmer, Alessandro; Silva, Joed Lopes da; Brandmeier, Thomas

doi:10.1109/itsc45102.2020.9294240

Cited by 8 publications

(10 citation statements)

References 14 publications

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“…In this case, we can make a comparison with [49]. This collects one of the largest European license plate datasets for license plate detection (exclusively).…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the biggest open-access European dataset is OpenALPR-EU [48], with only 108 images. The most comprehensive European dataset found is TLPD (THI license plate dataset), [49] with 18,000 labelled European license plates. The problem is that this dataset is not public access.…”

Section: Datasets: Problems and Solutionsmentioning

confidence: 99%

“…GAP-LP [46] Turkey 9175 Cars CCPD [36] China 250,000 Cars UFP-ALPR [47] China 4500 Various (cars, trucks, etc.) OpenALPR-EU [48] Europe 108 Various TLPD [49] Europe 18,000 Various…”

Section: Name Location Nº Of Images Vehiclesmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.

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“…In this case, we can make a comparison with [49]. This collects one of the largest European license plate datasets for license plate detection (exclusively).…”

Section: Resultsmentioning

confidence: 99%

Section: Datasets: Problems and Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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“…Since collecting, labeling, and classifying visual data is a tough process and requires much effort and time, there has always been a pale interest among researchers to develop large-scale datasets for computer vision and machine learning. Although there are some standard datasets available for license plate detection [9][10][11][12] and character recognition [13][14][15], they cannot be employed to recognize Farsi characters in images.…”

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 base de dados utilizada neste artigo é denominada THI License Plate Dataset (TLPD) (Chan et al, 2020). Este conjunto de dados é formado por imagens de veículos da Europa em diferentes cenários e condições climáticas.…”

Section: Base De Dadosunclassified

Detecção de placas de veículos com foco na proteção de dados pessoais

Souza

Zimmer²,

Freire

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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In recent years, there has been an increase in the development of systems related to data protection. Following the recent General Data Protection Regulation (GDPR), systems are being adapted to capture images without compromising the safety of users through improper disclosure. This paper intended to evaluate the performance of different versions of deep learning algorithms aimed at the detection of vehicle license plates. The research proposes the adaptation of one method to hide the license plates that were detected in images, thus providing the protection of personal data. The selected deep learning techniques were YOLO v3 (You Only Look Once) and YOLO v4. The training of the selected algorithms was performed with the THI License Plate Dataset (TLPD). The YOLO v3 algorithm reached an Average Precision (AP) equal to 98.68%, recall of 98.00%, and Average Intersection over Union (Average IoU) of 79.38%, being elected the best among the techniques compared in this study. Resumo: Nos últimos anos, percebe-se um aumento no desenvolvimento de sistemas relacionados à proteção de dados. Seguindo o recente Regulamento Geral de Proteção de Dados (GDPR), os sistemas estão sendo adaptados para capturar imagens sem comprometer a segurança dos usuários por meio de divulgação indevida. Este trabalho teve como objetivo avaliar o desempenho de diferentes algoritmos de aprendizado profundo voltados a detecção de placas de veículos. A pesquisa propõe a adaptação de um método para ocultar as placas presentes nas imagens, proporcionando assim a proteção dos dados pessoais. As técnicas de aprendizado profundo selecionadas foram YOLO v3 (You Only Look Once) e YOLO v4. O treinamento dos algoritmos selecionados foi realizado com o THI License Plate Dataset (TLPD). O algoritmo YOLO v3 atingiu um Average Precision (AP ) igual a 98,68%, recall de 98,00% e uma Average Intersection over Union (Average IoU ) de 79,38%, sendo eleita a melhor entre as técnicas comparadas neste estudo.

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European Union Dataset and Annotation Tool for Real Time Automatic License Plate Detection and Blurring

Cited by 8 publications

References 14 publications

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

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

Detecção de placas de veículos com foco na proteção de dados pessoais

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