Super Resolution of Car Plate Images Using Generative Adversarial Networks

Lai, Tan Kean; Abbas, Aymen Fadhil; Abdu, Aliyu Muhammad; Sheikh, Usman Ullah; Mokji, Musa Mohd; Kamal, Khalil

doi:10.1109/cspa.2019.8696010

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…In addition, our proposed SRGAN techniques, Model I and Model II, are compared with the reported earlier SRGAN architecture proposed by [42]. The proposed SRGAN design in this article is subtly different from the design used in the paper [42].…”

Section: E Evaluation Of the Performance Metricsmentioning

confidence: 99%

“…The SR image for LP was produced from blurry, tiny images as proposed by Lee et al [41]. Lai et al [42] suggested that the perceptual loss is a solution to the smoothing SR for the LP problem. Archiecture based on the Generator (G) network consists of 16 ResNet blocks with skip-connections using the ReLU activation function.…”

Section: ) Learning-based Approachesmentioning

confidence: 99%

“…The testing part of the car plate dataset is used to validate the quality of output images in terms of PSNR and SSIM metrics. These results are compared to the mentioned algorithms in section [IV-C] and other published methods [42]. After that, we tested the proposed models on the different datasets, ''Application-Oriented License Plate (AOLP) [6],'' and evaluated the recognition model (yolov5).…”

Section: B Dataset and Data Preprocessingmentioning

confidence: 99%

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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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Section: E Evaluation Of the Performance Metricsmentioning

confidence: 99%

Section: ) Learning-based Approachesmentioning

confidence: 99%

Section: B Dataset and Data Preprocessingmentioning

confidence: 99%

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

2022

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“…Before the recognition step, [33]- [35] all designed an image super-resolution module, which super-resolved LR images into high quality images to improve the performance of recognition. Note that, image super-resolution focuses on increasing the resolution of a given image to provide better visual quality for human viewing.…”

Section: Related Workmentioning

confidence: 99%

See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition

et al. 2020

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Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (RL-GAN) to generate super image representation that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our RL-GAN, which improves the classification results significantly, with 10-15% gain on average, compared with benchmark solutions.

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“…It uses image-processing techniques to automatically identify car plates from given input images without any human intervention. This system h as a very wide application area which includes traffic monitoring systems, car park access systems, traffic law enforcement systems, automatic toll paymen t systems, and border crossing control system [1][2][3]. In order to achieve a good car plate recognition result, the system must first be able to accurately localize the location of the car plate from the given input image.…”

Section: Introductionmentioning

confidence: 99%

Malaysian car plate localization using region-based convolutional neural network

Liang¹,

Sheikh²,

Mohd³

2020

Bulletin EEI

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Automatic car plate localization and recognition system is a system that identifies the car plate location and recognizes the characters on the car plate input images. Within the automated system, the car plate localization stage is the first stage and is the most crucial stage as the success rate of the whole system depends heavily on it. In this paper, a Malaysian car plate localization system using Region-based Convolutional Neural Network (R-CNN) is proposed. Using transfer learning on the AlexNet CNN, the localization was greatly improved achieving best precision and recall rate of 95.19% and 97.84% respectively. Besides, the proposed R-CNN was able to localize car plates in complex scenarios such as under occlusion.

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Super Resolution of Car Plate Images Using Generative Adversarial Networks

Cited by 7 publications

References 23 publications

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

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

See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition

Malaysian car plate localization using region-based convolutional neural network

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