Detection optimization of license plate targets based on AdvancedEAST

2021 ◽  
pp. 1-9
Author(s):  
Feifei Yin ◽  
Jingxuan Wang ◽  
Wei Xiong ◽  
Juanjuan Gao ◽  
Yu Gong
Keyword(s):  
Transfer Learning ◽  
Traffic Management ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Natural Scenes ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model ◽  
License Plate Detection

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.

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

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Chuan Pratama ◽  
Suci Aulia ◽  
Dadan Nur Ramadan ◽  
Sugondo Hadiyoso
Keyword(s):  
Detection Methods ◽  
Prototype System ◽  
License Plate ◽  
License Plate Recognition ◽  
Test Results ◽  
Accuracy Rate ◽  
Road Space ◽  
Camera Position ◽  
License Plate Detection ◽  
Automatic License Plate Recognition

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.

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.

IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION

2015 ◽  
Vol 3 (3) ◽  
pp. 296-301
Author(s):  
S. Madhan ◽  
M. Pradeep
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Stain Removal ◽  
License Plate Detection ◽  
Novel Methods ◽  
Automatic License Plate Recognition

This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.

scholarly journals An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

2020 ◽  
Vol 10 (6) ◽  
pp. 2165 ◽  
Author(s):  
Muhammad Ali Raza ◽  
Chun Qi ◽  
Muhammad Rizwan Asif ◽  
Muhammad Armoghan Khan
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Generalized Solution ◽  
Recognition System ◽  
Vital Role ◽  
Transport Systems ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

The Research and Improvement on Correction Algorithm of Inclination License Plate

2014 ◽  
Vol 543-547 ◽  
pp. 2800-2803
Author(s):  
Xiang Hua Hou ◽  
Hong Hai Liu
Keyword(s):  
Traffic Management ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Correction Algorithm ◽  
Segmentation Algorithms ◽  
License Plate Detection ◽  
Transportation Technologies ◽  
Detection And Recognition ◽  
Improved Algorithm

More and more intelligent transportation technologies are applied to license plate detection and recognition that can greatly reduce the burden of traffic management. However, character segmentation of license plate is an indispensable step of license plate recognition. Traditional character segmentation algorithms of license plate mainly use the space between characters of license plate to segment characters, but the license plate cannot be recognized if there are degraded characters or license plate inclination. In this paper, an improved character segmentation algorithm of license plate is proposed. In the improved algorithm, firstly, the noise of precise located license plate is eliminated and then the license plate inclination is tested. When there is license plate inclination, we calculate the inclination angle and use rotation to correct the inclination. So, the problem of license plate inclination is solved in character segmentation. Lastly, the results show that the improved algorithm has better effect than the traditional algorithms and it lays a good foundation for the next research of license plate recognition.

scholarly journals Vehicle License Plate Recognition

2021 ◽  
pp. 15-21
Author(s):  
Aman .. ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Recognition Rate ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
License Plate ◽  
License Plate Recognition ◽  
Single Class ◽  
Vehicle License Plate Recognition ◽  
Commercial Applications

One of the most significant parts of integrating computer technologies into intelligent transportation systems (ITS) is vehicle license plate recognition (VLPR). In most cases, however, to recognize a license plate successfully, the location of the license plate is to be determined first. Vehicle License Plate Recognition systems are used by law enforcement agencies, traffic management agencies, control agencies, and various government and non-government agencies. VLPR is used in various commercial applications, including electronic toll collecting, personal security, visitor management systems, parking management, and other corporate applications. As a result, calculating the correct positioning of a license plate from a vehicle image is an essential stage of a VLPR system, which substantially impacts the recognition rate and speed of the entire system. In the fields of intelligent transportation systems and image recognition, VLPR is a popular topic. In this research paper, we address the problem of license plate detection using a You Only Look Once (YOLO)-PyTorch deep learning architecture. In this research, we use YOLO version 5 to recognize a single class in an image dataset.

scholarly journals Fast Cartoon-Texture Decomposition Filtering Based License Plate Detection Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yingjun Wang ◽  
Chenping Zhao ◽  
Xiaoyan Liu ◽  
Mingfu Zhao ◽  
Linfeng Bai
Keyword(s):  
High Efficiency ◽  
Detection Method ◽  
Correction Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
License Plate Recognition ◽  
Background Interference ◽  
Morphological Filtering ◽  
License Plate Detection

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency.

scholarly journals Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method

2020 ◽  
Vol 26 (7) ◽  
pp. 115-126
Author(s):  
Bydaa Ali Hussain ◽  
Mohammed Sadoon Hathal
Keyword(s):  
Edge Detection ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Detection Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
On The Road

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

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