scholarly journals A New Approach for License Plate Detection and Localization: Between Reality and Applicability

2015 ◽  
Vol 8 (11) ◽  
pp. 13 ◽  
Author(s):  
Akram A. Moustafa ◽  
Mohammed-Issa Riad Mousa Jaradat
Keyword(s):  
Traffic Management ◽  
Traffic Signals ◽  
Recognition System ◽  
License Plate ◽  
New Approach ◽  
Morphological Operators ◽  
License Plate Detection ◽  
Traffic Management System ◽  
Detection And Localization

<p>License Plate Detection and Localization (LPDL) is known to have become one of the most progressive and growing areas of study in the field of Intelligent Traffic Management System (ITMS). LPDL provides assistance by being able to specifically locate a vehicle’s number plate which is an essential part of ITMS, that is used for automatic road tax collection, traffic signals defilement implementation, borders and payments barriers and to monitor unlike activities. Organizations can deploy the number plate detection and recognition system to track their vehicles and to monitor each of them in their vital business activities like inbound and outbound logistics, find the exact location of their vehicles and organize entrance management. A competent algorithm is proposed in this paper for number plate detection and localization based on segmentation and morphological operators. Thus, the proposed algorithm it works on enhancing the quality of the image by applying morphological operators afterwards to segment out license plate from the captured image. No assumptions about the license plate color, style of font, size of text and type of material the plate is made of. The results reveal that the proposed algorithm works perfectly on all kinds of license plates with 93.43% efficiency rate. </p>

Research of License Plate Recognition System Based on Labview

2015 ◽  
Vol 734 ◽  
pp. 646-649
Author(s):  
Zhong Hua Hu ◽  
Chen Tang
Keyword(s):  
Virtual Instrument ◽  
Character Recognition ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
License Plate ◽  
Morphological Operations ◽  
License Plate Recognition ◽  
Plate Location ◽  
Traffic Management System

The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

scholarly journals Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichao Wang ◽  
Yu Jiang ◽  
Jiaxin Liu ◽  
Siyu Gong ◽  
Jian Yao ◽  
...  
Keyword(s):  
Traffic Management ◽  
Recognition Rate ◽  
Recognition System ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Generalization Ability ◽  
Traffic Management System ◽  
High Recognition Accuracy ◽  
Fully Connected

The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm—which has strong generalization ability, scalability, and robustness—performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.

Edge Features and Geometrical Properties Based Approach for Vehicle License Plate Detection and Localization

2012 ◽  
Vol 4 (2) ◽  
pp. 63-75
Author(s):  
Hinde Anoual ◽  
Sanaa El Fkihi ◽  
Abdelilah Jilbab ◽  
Driss Aboutajdine
Keyword(s):  
Recognition System ◽  
Recall Rate ◽  
License Plate ◽  
Flow Management ◽  
Geometrical Properties ◽  
Time Record ◽  
License Plate Detection ◽  
Edge Features ◽  
Detection And Localization ◽  
Precision Rate

Frequently, a need exists to identify vehicle license plates (VLP) for security. The extracted information from VLP is used for enforcement, access-control, and flow management, e.g., to keep a time record for automatic payment calculations or fight crime, making license plate detection crucial and inevitable in the VLP recognition system. This paper presents a robust method to detect and localize license plates in images. Specifically, the authors examine Moroccans’ VLPs. The proposed approach is based on edge features and characteristics of license plate characters. Various images including Moroccans’ VLPs were used to evaluate the proposed method. The experimental results show that the authors’ system can efficiently detect and localize the VLP in the images. Indeed, the recall/precision curve proves that 95% precision rate is obtained for recall rate value equals to 81%. In addition, the standard measure of quality is equal to 87.44%.

scholarly journals Reconfigurable SRTM System for Road Traffic in Kingdom of Bahrain

2016 ◽  
Vol 17 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Wael El-Medany ◽  
Alauddin Al-Omary ◽  
Riyadh Al-Hakim ◽  
Taher Homeed
Keyword(s):  
Traffic Management ◽  
Road Traffic ◽  
Traffic Signals ◽  
Google Maps ◽  
Traffic System ◽  
The Road ◽  
Field Programmable ◽  
Hardware Description ◽  
Traffic Management System ◽  
On The Road

Abstract This paper presents reconfigurable hardware architecture for smart road traffic system based on Field Programmable Gate Array (FPGA). The design can be reconfigured for different timing of the traffic signals according to the received and collected data read by the different sensors on the road; the design has been described using VHDL (VHSIC Hardware Description Language). The SRTM (Smart Road Traffic Management) System has some more features that help passenger to avoid traffic jamming by sending the collected information through web/mobile applications to find the best road between the start and destination points, which will be displayed on Google maps, at the same time it will also shows the points of traffic jamming on Google maps. SRTM system can also manage emergency vehicles such as ambulance and fire fighter and also can send snapshots and video streaming for different roads and junctions to show the points of traffic jamming. The design has been simulated and tested using ModelSim PE student edition 10.4. Spartan 3 FPGA starter kit from Xilinx has been used for implementing and testing the design in a hardware level.

scholarly journals Fused faster RCNNs for efficient detection of the license plates

2020 ◽  
Vol 19 (2) ◽  
pp. 874
Author(s):  
Naaman Omar ◽  
Adnan Mohsin Abdulazeez ◽  
Abdulkadir Sengur ◽  
Salim Ganim Saeed Al-Ali
Keyword(s):  
Traffic Safety ◽  
Performance Indicator ◽  
License Plate ◽  
Traffic Surveillance ◽  
New Approach ◽  
Security Services ◽  
Efficient Detection ◽  
Parking Management ◽  
License Plate Detection ◽  
Traditional Image

Automatic License Plate Detection and Recognition (ALPD-R) is an important and challenging application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. Although the general approaches perform well on ALPD-R, new and efficient approaches are needed to improve the detection accuracies. Thus, in this paper, a new approach, which is based on fusing of multiple Faster Regions with Convolutional Neutral Network (Faster- RCNN) architectures, is proposed. More specially, the Deep Learning (DL) is used to detect license plates in given images. The proposed license plate detection method uses three Faster- RCNN modules where each faster RCNN module uses a pre-trained CNN model namely AlexNet, VGG16 and VGG19. Each Faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the Faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

scholarly journals Vehicle License Plate Detection and Recognition System

2016 ◽  
Vol 137 (9) ◽  
pp. 31-34 ◽  
Author(s):  
Poonam Bhogale ◽  
Archit Save ◽  
Vitrag Jain ◽  
Saurabh Parekh
Keyword(s):  
Recognition System ◽  
License Plate ◽  
License Plate Detection ◽  
Detection And Recognition

Research on Pre-Processing Methods for License Plate Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 47-79
Author(s):  
Weifang Zhai ◽  
Terry Gao ◽  
Juan Feng
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Traffic Management ◽  
Recognition System ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
License Plate Location ◽  
Plate Location

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

scholarly journals Smart Alarm Traffic Management System for the Major Road to Isolation Hospital during the Covid-19 Pandemic in Port Said City

2021 ◽  
Vol 10 (1) ◽  
pp. 282-288
Keyword(s):  
Quality Of Life ◽  
Traffic Management ◽  
Respiratory Symptoms ◽  
Major Road ◽  
Smart Transportation ◽  
Congested Traffic ◽  
Traffic Management System ◽  
Smart Traffic ◽  
Port Said

Transportation is an important feature that affects the quality of life. Huge increase in population, modernization in all aspects of life, and cities expansion lead to a more congested traffic that may be acceptable for in-emerging trips but enormous for emergency trips, especially for COVID 19 patients with severe respiratory symptoms. Smart transportation techniques offer solutions to the congestion problemsfor different modes of transportation and traffic management. In this paper, a smart traffic solution to the congestion problem in the major road to isolation hospital in Port Said City is presented.

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