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 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>

scholarly journals Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

2020 ◽  
Vol 10 (4) ◽  
pp. 5979-5985
Author(s):  
M. Salemdeeb ◽  
S. Erturk
Keyword(s):  
Network Architecture ◽  
Real Life ◽  
Low Complexity ◽  
License Plate ◽  
Neural Network Architecture ◽  
New Approach ◽  
Computer Vision Applications ◽  
License Plate Detection ◽  
Language Detection ◽  
Detection And Recognition

Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.

License plate detection and recognition based on YOLOv3 and ILPRNET

2021 ◽  
Author(s):  
Yongjie Zou ◽  
Yongjun Zhang ◽  
Jun Yan ◽  
Xiaoxu Jiang ◽  
Tengjie Huang ◽  
...  
Keyword(s):  
License Plate ◽  
License Plate Detection ◽  
Detection And Recognition
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