Automatic and Secure Electronic Gate System Using Fusion of License Plate, Car Make Recognition and Face Detection

Saadouli, Ghaida; Elburdani, Maha Ibrahim; Al-Qatouni, Razan Mohammed; Kunhoth, Suchithra; Al‐Maadeed, Somaya

doi:10.1109/iciot48696.2020.9089615

Cited by 17 publications

(18 citation statements)

References 25 publications

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“…There's a requirement however for segmentation datasets annotated in polygonal format capturing enhanced contextual features of a vehicle that is not available as of now. With the aim of privacy in the perspective of application to public security, utilized in this paper is a dataset from [18] for instance segmentation of the frontal part of the car which includes, segmentation, detection, and classification. Classification of make is performed using traditional rule-based approaches which are dominant in this field due to the popularity of the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content. both deep learning and machine learning approaches [18]. A pre-set unique features from these images are extracted for machine learning algorithm where as auto-feature extraction is performed by the deep learning models.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Ottakath¹,

Al-Maadeed²

2023

Preprint

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Vehicle identification is an important task in traffic monitoring because it allows for efficient inference and provides a cause for action. Vehicle classification via deep learning and other approaches such as segmentation is a critical tool for re-identification. In this paper, instance segmentation is used to identify vehicle makes with license plate detection, allowing for better unique vehicle recognition for re-identification. A dataset is annotated and modified, for example, by segmenting it with polygonal bounding boxes that capture the vehicle's unique frontal features. In addition, license plate localization is performed. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches based on CNN and deformed CNN. Furthermore, a deep residual network and fully connected layer-based classification were utilized as the backbone for feature representation. Instance segmentation detects objects by segmenting and classifying regions of interest. The imbalance in the dataset is resolved using a mosaic-tiled approach, which produces greater precision than other approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Ottakath¹,

Al-Maadeed²

2023

Preprint

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“…Secondly, segmenting the whole plate into single blocks of character [13]. Thirdly, recognize the segmented characters with handcrafted features [14], [15] and pre-designed classifiers like Support Vector Machine (SVM) [16], Naive Bayes algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)

2022

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Although the majority of existing License Plate (LP) recognition techniques have significant improvements in accuracy, they are still limited to ideal situations in which training data is correctly annotated with restricted scenarios. Moreover, images or videos are frequently used in monitoring systems that have Low Resolution (LR) quality. In this work, the problem of LP detection in digital images is addressed in the images of a naturalistic environment. Single-stage character segmentation and recognition are combined with adversarial Super-Resolution (SR) approaches to improve the quality of the LP by processing the LR images into High-Resolution (HR) images. This work proposes effective changes to the SRGAN network regarding the number of layers, an activation function, and the appropriate loss regularization using Total Variation (TV) loss. The main paper contribution can be summarized into presenting an end-to-end deep learning framework based on generative adversarial networks (GAN), which is able to generate realistic super-resolution images. Also, proposed adding a TV regularization to the loss function to help the model enhance the resolution of images. The proposed SRGAN can handle tiny 72 × 72 images of LPs. The paper explores how SRGAN performed over different datasets from many aspects, such as visual analysis, PSNR, SSIM, and Optical Character Recognition (OCR). The experiments demonstrate that the suggested SRGAN can generate high-resolution images that improve the accuracy of the license plate recognition stage compared to other systems.

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“…One of the researchers dealt with LPN as a single image without extracting their numbers. Then match them with other LPN in the database using different algorithms, like matching templates [18], SIFT [19], CNN [5], etc. Or the letters are extracted from the PN and then matched using the OCR [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between the Use of CNN and Matching Templates in Recognizing the Iraqi License Plate Number

Hmdaoy¹,

Ahmed²

2022

ANJS

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One of the important applications that organize people's lives is the applications that relate to organizing the traffic regulation, detecting violators, detecting stolen cars, and managing car parks. Recognizing the license plate number (LPN) crosses the basic process in all previous applications. This process is affected by the surrounding light conditions while taking an image of the car. In addition to these problems, the plates of modern Iraqi cars (the so-called German number) suffer from words in silver color printed on the plate, which causes a senior problem when locating the LPN and recognizing it. To solve these problems, we presented in this research a comparison between the use of the SIFT and Contour algorithm in locating the LPN. While using CNN training models to recognize the PN achieved higher results than template matching.

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Automatic and Secure Electronic Gate System Using Fusion of License Plate, Car Make Recognition and Face Detection

Cited by 17 publications

References 25 publications

Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)

A Comparison Between the Use of CNN and Matching Templates in Recognizing the Iraqi License Plate Number

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