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

2020 ◽  
pp. 425-438 ◽  
Author(s):  
Saeed Khazaee ◽  
Ali Tourani ◽  
Sajjad Soroori ◽  
Asadollah Shahbahrami ◽  
Ching Y. Suen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
License Plate ◽  
Learning Approach ◽  
License Plate Detection

Brazilian Mercosur License Plate Detection: a Deep Learning Approach Relying on Synthetic Imagery

2019 ◽  
Author(s):  
Vinicius Ribeiro ◽  
Vitor Greati ◽  
Aguinaldo Bezerra ◽  
Gilles Silvano ◽  
Ivanovitch Silva ◽  
...  
Keyword(s):  
Deep Learning ◽  
License Plate ◽  
Learning Approach ◽  
License Plate Detection

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

IEEE Access ◽  
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

Deep‐learning‐based license plate detection method using vehicle region extraction

2017 ◽  
Vol 53 (15) ◽  
pp. 1034-1036 ◽  
Author(s):  
S.G. Kim ◽  
H.G. Jeon ◽  
H.I. Koo
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
License Plate ◽  
Region Extraction ◽  
License Plate Detection

Real Time License Plate Recognition from Video Streams using Deep Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 65-87
Author(s):  
Saquib Nadeem Hashmi ◽  
Kaushtubh Kumar ◽  
Siddhant Khandelwal ◽  
Dravit Lochan ◽  
Sangeeta Mittal
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Urban Areas ◽  
Character Recognition ◽  
License Plate ◽  
Learning Approach ◽  
Complex Task ◽  
License Plate Recognition ◽  
Video Streams ◽  
Number Recognition

With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.

scholarly journals A Real-time Pothole Detection Based on Deep Learning Approach

2021 ◽  
Vol 1828 (1) ◽  
pp. 012001
Author(s):  
Yeoh Keng Yik ◽  
Nurul Ezaila Alias ◽  
Yusmeeraz Yusof ◽  
Suhaila Isaak
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Approach ◽  
Pothole Detection

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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