Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Alam, Nur-A; Ahsan, Mominul; Based, Md. Abdul; Haider, Julfikar

doi:10.3390/technologies9010009

Cited by 52 publications

(17 citation statements)

References 35 publications

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“…In [68], a method using Convolutional Neural Network (CNN) and Deep Learning strategies to identify and recognize vehicle number plates in the Bengali language has been suggested by Alam. A super-resolution technique has been used with the CNN in the recognition portion to reconstruct the pixel quality of the input image.…”

Section: Related Work On Vehicle Number Plate Detection and Recognition Techniquesmentioning

confidence: 99%

Vehicle Number Plate Detection and Recognition Techniques: A Review

Parvin¹,

Rozario²,

Islam³

2021

Adv. sci. technol. eng. syst. j.

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Vehicle number plate detection and recognition is an integral part of the IntelligentTransport System (ITS) as every vehicle has a number plate as part of its identity. The quantity of vehicles on road is growing in the modern age, so numerous crimes are also increasing day by day. Almost every day the news of missing vehicles and accidents are perceived. Vehicles tracking is often required to investigate all these illegal activities. So, vehicle number plate identification, as well as recognition, is an active field of study. However, vehicle number plate identification has always been a challenging task for some reasons, for example, brightness changes, vehicle shadows, and non-uniform license plate character type, various styles, and environment color effects. In this review work, various state-of-the-art vehicle number plate detection, as well as recognition strategies, have been outlined on how researchers have experimented with these techniques, which methods have been developed or used, what datasets have been focused on, what kinds of characters have been recognized and how much progress have been achieved. Hopefully, for future research, this review would be very useful.

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Section: Related Work On Vehicle Number Plate Detection and Recognition Techniquesmentioning

confidence: 99%

Vehicle Number Plate Detection and Recognition Techniques: A Review

Parvin¹,

Rozario²,

Islam³

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…is creates a vicious circle of training, which is not in line with the original intention of model training. In order to solve this problem, we carry out weight control on the types of imbalances, and the hierarchical loss calculation is given as follows [12]:…”

Section: Pspnet Offline Detectionmentioning

confidence: 99%

“…As a fusion product of big data technology and artificial intelligence technology, deep network provides a new solution for the detection of complex traffic image violations [11,12]. e deep network model uses the multilayer network structure to train and learn the image data set collected by ITS through the continuous training and learning of the multilayer network to realize effective judgment of violations.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Yang

2021

Scientific Programming

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Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.

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“…The attribute extraction model and the BPNN Backpropagation Neural Network are used in this methodology for license plate identification and character algorithm. There are three stages of recognizing license plates: Localization of license plates, character segmentation, designation, and identification For machine learning classification, the Support Vector Machine approach (SVM) [13]and neural network techniques such as BPNN are used.…”

Section: B a Robust License Plate Detection And Character Recognition Algorithm Using A Bpnn And A Feature Extraction Modelmentioning

confidence: 99%

Performance Analysis on Automatic Number Plate Recognition System and Methods

2021

IJATCSE

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Now a days, vehiclesareentering the educational institutes or other organizations without taking a proper record. It is necessary to implement a system which is used to detect the vehicle number plate and recognize it, is this vehicle registered in this institute or not. Automatic Number Plate Recognition (ANPR) System used to resolve this issue. This system is useful to Parking fee collection, Barrier crossing, Finding the stolen cars and so on. Some condition may be affectthis due to low light or weather condition, size of thenumber plate also matter. This paper showsthe multiple techniques to implement this system and also show the results of Automatic Number Plate Recognition system

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Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Cited by 52 publications

References 35 publications

Vehicle Number Plate Detection and Recognition Techniques: A Review

Vehicle Number Plate Detection and Recognition Techniques: A Review

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Performance Analysis on Automatic Number Plate Recognition System and Methods

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