Detection of the vehicle license plate using a kernel density with default search radius algorithm filter

Davix, X. Ascar; Christopher, C. Seldev; Judson, D.

doi:10.1016/j.ijleo.2020.164689

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Symbols comparable to plates such as numerals imprinted on a car and bumpers with upright patterning may appear in the image backdrop. Thus, the identification of number plates is complicated by differences in platter kinds or surroundings 8 …”

Section: Introductionmentioning

confidence: 99%

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A hybrid location‐dependent ultra convolutional neural network‐based vehicle number plate recognition approach for intelligent transportation systems

Ramasamy¹,

Selvarajan²,

Kaliyaperumal

et al. 2023

Concurrency and Computation

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Summary In today's world, identifying the owner and proprietor of a vehicle that violates driving rules or does any unintentional work on the street is a challenging task. Inspection of each driver's license number takes a long time for a highway police officer. To overcome this, many researchers have introduced an automated number plate recognition approach which is usually a computer vision‐based technique to identify the vehicle's registration plate. However, the existing recognition approaches are lagged to extract the influential features which degrade the detection accuracy and increase the misclassification errors. In this article, a novel automated number plate recognition methodology has been proposed to identify the number plates accurately with minimal error rates. Primary, a new pretrained location‐dependent ultra convolutional neural network (LUCNN) is employed to learn the influential features from the input images. These obtained features are then fed into hybrid single‐shot fully convolutional detectors with a support vector machine (SSVM) classifier to separate the vehicle's city, model, and number from the registration location. At varied automobile distances, the proposed LUCNN + SSVM model is able to retrieve the number plate regions in the picture acquired from its back end. The performance results manifest that the proposed LUCNN + SSVM model attains a better accuracy of 98.75% and a lesser error range of 1.25% than the existing recognition models.

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Section: Introductionmentioning

confidence: 99%

“…Thus, the identification of number plates is complicated by differences in platter kinds or surroundings. 8 In the last few decades, several researchers have introduced an automated NPR model based on image processing. 9 Most image processing-based models consider a variety of image attributes like color features, edge features, and texture features.…”

mentioning

confidence: 99%

A hybrid location‐dependent ultra convolutional neural network‐based vehicle number plate recognition approach for intelligent transportation systems

Ramasamy¹,

Selvarajan²,

Kaliyaperumal

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

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“…e currently used methods and technologies of character location include convolutional neural networks (CNNs), color segmentation, multilayer self-coding combined with SVM, and constrained AdaBoost algorithm and binary technology of kernel density function preprocessing [3][4][5][6][7][8][9]. In order to identify the characters in the license plate quickly and accurately, HAAR features were used to train AdaBoost classifier for finding the characters' location, and the separated characters were recognized by the trained BP neural network [3].…”

Section: Introductionmentioning

confidence: 99%

“…In order to identify the characters in the license plate quickly and accurately, HAAR features were used to train AdaBoost classifier for finding the characters' location, and the separated characters were recognized by the trained BP neural network [3]. Compared with the traditional neural network detection method, the positioning detection of steel plate and slab number based on MobileNet acceleration model also improved the detection speed and reduced the network weights [4][5][6][7][8][9][10]. LocNet-based positioning module could replace bounding box regression module to enhance the positioning accuracy of text detector.…”

Section: Introductionmentioning

confidence: 99%

Marker Identification of Disc Workpiece Based on HOG-SVM Classifier

Liu

Gao

Zhao

et al. 2022

Mobile Information Systems

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With the less humanized trend of manufacturing production line gradually progressing, real-time recording and tracking of workpieces information have become an essential way to implement efficient management. To collect and identify the workpiece marker is of great significance for product information statistics and quality traceability. Aiming at the problems of inaccurately positioning, difficultly segmenting, and slowly recognizing for capturing the marker of disc workpieces distributed circularly, this study proposes a projection segmentation algorithm based on polar coordinate inverse transformation to locate and separate the circularly arranged marker and extract the total separated characters. The features of the directional gradient histogram (HOG) of characters are used as the input of the support vector machine (SVM) model, and after training, a workpiece marker recognition classifier is obtained. The experiment results show that the recognition accuracy of markers composed of letters and numbers is over 97% by the proposed method. Our proposed method outperformed state-of-the-art approaches in achieving higher recognition accuracy rate with the SVM classifier.

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