PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool

Practical Applications ◽

License Plate Detection ◽

Detection And Recognition ◽

Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.

Automatic License Plate Recognition: An Efficient and Layout-Independent System Based on the YOLO Detector

10.5753/sibgrapi.est.2020.12978 ◽

2020 ◽

Author(s):

Rayson Laroca ◽

David Menotti

Keyword(s):

Law Enforcement ◽

License Plate ◽

Unified Approach ◽

Practical Applications ◽

Major Shortcoming ◽

License Plate Detection ◽

Traffic Law Enforcement ◽

Public Datasets ◽

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and that leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR, called UFPR-ALPR, that has become very popular, having been downloaded more than 650 times by researchers from 80 different countries over the past two years. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature. The entire ALPR system (i.e., the architectures and weights), along with all annotations made by us are publicly available at https://web.inf.ufpr.br/vri/publications/layout-independent-alpr/.

Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector

10.5753/ctd.2020.11372 ◽

2020 ◽

Author(s):

Rayson Laroca ◽

David Menotti

Keyword(s):

Law Enforcement ◽

License Plate ◽

Unified Approach ◽

Practical Applications ◽

Major Shortcoming ◽

License Plate Detection ◽

Traffic Law Enforcement ◽

Public Datasets ◽

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR that has become very popular, having been downloaded more than 550 times by researchers from 76 different countries in the last year alone. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature.

Object Detection: Automatic License Plate Detection using Deep Learning and OpenCV

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1842.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6022-6028

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Object Detection ◽

Weather Condition ◽

License Plate ◽

Practical Applications ◽

Private And Public ◽

Self Driving Cars ◽

Object Detection is one of the most important concepts of Computer Vision which is used in various areas like Medical Field, Security, Self Driving cars, Automated vehicle systems etc.We choose the application of Automatic License plate Recognition. Automatic License Plate Recognition is an emerging technology which is helpful in many fields and at the same time is challenging. It’s challenging because we need to get the accurate recognition of the characters in a number plate. In practical applications where sometimes the images are captured in the worst weather condition, bad lighting, wind. And to the addition, license plates are often dirty or blackened due to the smoke, half broken , or having scratches on certain characters and detection of too many license plates in a single frame. All these will act as the obstacles in developing an effective ALPR system. So basically, this is a system where recognition of characters from images using Computer Vision techniques are performed. This system is implemented in many fields like parking lots, private and public entrances, toll gates, theft control, checking the authentication of a vehicle. Procedure followed in this paper are, first capturing images from camera then loading that into system, preprocessing done using OpenCV library. Then we use Attention OCR a deep learning model to recognize the characters from an image. And later display that in the GUI and store them in the databases for different operations later.

Using Synthetic Images for Deep Learning Recognition Process on Automatic License Plate Recognition

Lecture Notes in Computer Science - Pattern Recognition ◽

10.1007/978-3-030-21077-9_11 ◽

2019 ◽

pp. 115-126

Author(s):

Saulo Cardoso Barreto ◽

Jorge Albuquerque Lambert ◽

Flávio de Barros Vidal

Keyword(s):

Deep Learning ◽

License Plate ◽

Recognition Process ◽

Synthetic Images ◽

A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

IEEE Access ◽

10.1109/access.2020.3035992 ◽

2020 ◽

Vol 8 ◽

pp. 201317-201330

Author(s):

Ali Tourani ◽

Asadollah Shahbahrami ◽

Sajjad Soroori ◽

Saeed Khazaee ◽

Ching Yee Suen

Keyword(s):

Deep Learning ◽

License Plate ◽

Learning Approach ◽

Surveillance Systems ◽

License Plate Detection ◽

Detection And Recognition

Research on License Plate Detection and Recognition Based on Deep Learning

10.1109/icceai52939.2021.00081 ◽

2021 ◽

Author(s):

Lei Gao ◽

Weibin Zhang

Keyword(s):

Deep Learning ◽

License Plate ◽

License Plate Detection ◽

Detection And Recognition

A Deep Learning Solution for Integrated Traffic Control Through Automatic License Plate Recognition

10.1007/978-3-030-86970-0_16 ◽

2021 ◽

pp. 211-226

Author(s):

Riccardo Balia ◽

Silvio Barra ◽

Salvatore Carta ◽

Gianni Fenu ◽

Alessandro Sebastian Podda ◽

...

Keyword(s):

Deep Learning ◽

Traffic Control ◽

License Plate ◽

Automatic License Plate Recognition Using Deep Learning

Advances in Data Science - Communications in Computer and Information Science ◽

10.1007/978-981-13-3582-2_4 ◽

2018 ◽

pp. 46-58

Author(s):

Bhavin Dhedhi ◽

Prathamesh Datar ◽

Anuj Chiplunkar ◽

Kashish Jain ◽

Amrith Rangarajan ◽

...

Keyword(s):

Deep Learning ◽

License Plate ◽

An embedded automatic license plate recognition system using deep learning

Design Automation for Embedded Systems ◽

10.1007/s10617-019-09230-5 ◽

2019 ◽

Vol 24 (1) ◽

pp. 23-43 ◽

Cited By ~ 3

Author(s):

Diogo M. F. Izidio ◽

Antonyus P. A. Ferreira ◽

Heitor R. Medeiros ◽

Edna N. da S. Barros

Keyword(s):

Deep Learning ◽

Recognition System ◽

License Plate ◽

Vehicle License Plate Detection for Parking Offenders Using Automatic License-Plate Recognition

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.4.32 ◽

2020 ◽

Vol 55 (4) ◽

Author(s):

Chuan Pratama ◽

Suci Aulia ◽

Dadan Nur Ramadan ◽

Sugondo Hadiyoso

Keyword(s):

Detection Methods ◽

Prototype System ◽

License Plate ◽

Test Results ◽

Accuracy Rate ◽

Road Space ◽

Camera Position ◽

License Plate Detection ◽

Vehicles parked illegally on the highway can limit road space and result in congestion. Thus, illegal parking must be monitored and controlled. In this study, a prototype system for detecting the license plates of parking offenders based on image processing was implemented. The first stage in this system is detecting the license plate, then segmenting each character into a separate image. The next stage is converting the character from image to text format, referred to as automatic license-plate recognition. The goal is to send that detected plate license to the database of the authorities, so that the authorities can discover the identity of the parking offender to impose sanctions. In this study, several conditions of acquisition and variations of edge detection methods were tested. Based on the test results, an accuracy rate of 100% was obtained for license plate detection using the Canny method during the morning, with the camera position at 3 meters high, 2 meters of distance, and a 60o angle.