scholarly journals A Live-Video Automatic Number Plate Recognition (ANPR) System Using Convolutional Neural Network (CNN) with Data Labelling on an Android Smartphone

2021 ◽  
Vol 11 (10) ◽  
pp. 88-95
Author(s):  
Abd Gani S. F. ◽  
◽  
Miskon M. F ◽  
Hamzah R. A ◽  
Mohamood N ◽  
...  
Keyword(s):  
Outdoor Environment ◽  
Computational Time ◽  
License Plate ◽  
Live Video ◽  
Camera Angle ◽  
Processing Power ◽  
Software Algorithms ◽  
Parking Management ◽  
Traffic Violations ◽  
Electronic Hardware

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.

Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System

2021 ◽  
Vol 9 (1) ◽  
pp. 69
Author(s):  
I Kadek Gunawan ◽  
I Putu Agung Bayupati ◽  
Kadek Suar Wibawa ◽  
I Made Sukarsa ◽  
Laurensius Adi Kurniawan
Keyword(s):  
Computation Time ◽  
Computational Time ◽  
Video Frame ◽  
License Plate ◽  
Morphological Operations ◽  
Horizontal Projection ◽  
Validation Data ◽  
Plate Number ◽  
Parking Management ◽  
Plate Area

A vehicle registration plate is used for vehicle identity. In recent years, technology to identify plate numbers automatically or known as Automatic License Plate Recognition (ALPR) has grown over time. Convolutional Neural Network and   YOLACT are used to do plate number recognition from a video. The number plate recognition process consists of 3 stages. The first stage determines the coordinates of the number plate area on a video frame using YOLACT. The second stage is to separate each character inside the plat number using morphological operations, horizontal projection, and topological structural. The third stage is recognizing each character candidate using CNN MobileNetV2. To reduce computation time by only take several frames in the video, frame sampling is performed. This experiment study uses frame sampling, YOLACT epoch, MobileNet V2 epoch, and the ratio of validation data as parameters. The best results are with 250ms frame sampling succeed to reduce computational times up to 78%, whereas the accuracy is affected by the MobileNetV2 model with 100 epoch and ratio of split data validation 0,1 which results in 83,33% in average accuracy. Frame sampling can reduce computational time however higher frame sampling value causes the system fails to obtain plate region area.

Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera

2013 ◽  
Vol 300-301 ◽  
pp. 740-745
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Chiu Ching Tuan ◽  
You Ru Wen ◽  
Wen Tzeng Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Recognition System ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Hardware Cost ◽  
Localization System ◽  
Parking Management ◽  
Toll Collection ◽  
Localization Systems

LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

scholarly journals Vehicle License Plate Detection and Perspective Rectification

2019 ◽  
Vol 25 (5) ◽  
pp. 47-56 ◽  
Author(s):  
Musaed Alhussein ◽  
Khursheed Aurangzeb ◽  
Syed Irtaza Haider
Keyword(s):  
Correction Method ◽  
Traffic Monitoring ◽  
Character Segmentation ◽  
Outdoor Environment ◽  
License Plate ◽  
Correction Algorithm ◽  
Vehicle Identification ◽  
Corner Points ◽  
Considerable Impact ◽  
License Plate Detection

The character segmentation and perspective rectification of Vehicle License Plate (VLP) is essential in different applications, including traffic monitoring, car parking, stolen vehicle recovery, and toll payment. The character segmentation of the VLP and its horizontal as well as vertical (pan and tilt) correction is a crucial operation. It has considerable impact on the precision of the vehicle identification process. In this work, we investigate an effective framework for the perspective rectification and homography correction of vehicle's images. The captured images of the vehicle could be tilted in vertical or horizontal or vertical-horizontal mix directions due to different movements. For reasonable high identification results, a polynomial fitting based homography correction method for rectifying the tilted VLPs is applied. A method for determining four corner points of the rotated VPLs is explored. These four detected corner points are applied in the homography correction algorithm. For comprehensively evaluating the performance of the proposed framework, the detected VLPs in various directions, such as horizontal, vertical, and mix horizontal-vertical, are rotated. For the experiments, the real images of the vehicles in the outdoor environment, from different directions and different distances are captured. With our proposed method, we achieve an accuracy of 97 % and 95 % for the simulated and real captured images, respectively.

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 Bengali License Plate Detection using Viola-Jones Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 4058-4065
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Road Safety ◽  
License Plate ◽  
Learning Techniques ◽  
Wide Range ◽  
Previous Decade ◽  
Parking Management ◽  
License Plate Detection ◽  
Individual Classifier

Image classification has been a rapidly developing field over the previous decade, and the utilization of Convolutional Neural Networks (CNNs) and other deep learning techniques is developing rapidly. However, CNNs are compute-intensive. Another algorithm which was broadly utilized and keeps on being utilized is the Viola-Jones algorithm. Viola-Jones adopts an accumulation strategy. This means Viola-Jones utilizes a wide range of classifiers, each looking at a different part of the image. Every individual classifier is more fragile than the last classifier since it is taking in fewer data. At the point when the outcomes from every classifier are joined, be that as it may, they produce a solid classifier. In this paper, we would like to develop a model that will be able to detect the Bengali license plates of, using the Viola-Jones Algorithm with better precision. It can be utilized for various purposes like roadside help, road safety, parking management, etc

scholarly journals A Low-Cost On-Street Parking Management System Based on Bluetooth Beacons

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4559 ◽  
Author(s):  
Chi-Fang Chien ◽  
Hui-Tzu Chen ◽  
Chi-Yi Lin
Keyword(s):  
Management System ◽  
Low Cost ◽  
Distance Estimation ◽  
Magnetic Sensors ◽  
License Plate ◽  
City Governments ◽  
Human Labor ◽  
Parking Management ◽  
Information And Communication ◽  
Smart Parking

In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. However, using wireless magnetic sensors can only detect the occupancy of parking spaces without the knowledge of who are currently using these parking spaces; human labor is still needed to issue the parking bills. In contrast, smart parking meters based on image recognition can detect the occupancy of parking spaces along with the license plate numbers, but the cost of deploying smart parking meters is relatively high. In this research, we investigate the feasibility of building an on-street parking management system mainly based on low-cost Bluetooth beacons. Specifically, beacon transmitters are installed in the vehicles, and beacon receivers are deployed along the roadside parking spaces. By processing the received beacon signals using Kalman filter, our system can detect the occupancy of parking spaces as well as the identification of the vehicles. Although distance estimation using the received signal strength is not accurate, our experiments show that it suffices for correct detection of parking occupancy.

scholarly journals A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chun-Liang Tung ◽  
Ching-Hsin Wang ◽  
Bo-Syuan Peng
Keyword(s):  
Data Processing ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
License Plate ◽  
License Plate Recognition ◽  
Processing Industry ◽  
Recognition Model ◽  
Traffic Violations ◽  
Detection Stage ◽  
Plate Location

Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.

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