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Advancements and Challenges: A Comprehensive Review of GAN-based Models for the Mitigation of Small Dataset and Texture Sticking Issues in Fake License Plate Recognition

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dc.citedby0
dc.contributor.authorHabeeb D.en_US
dc.contributor.authorAlhassani A.H.en_US
dc.contributor.authorAbdullah L.N.en_US
dc.contributor.authorDer C.S.en_US
dc.contributor.authorAlasadi L.K.Q.en_US
dc.contributor.authorid57219414936en_US
dc.contributor.authorid57214839695en_US
dc.contributor.authorid25633835000en_US
dc.contributor.authorid58510587900en_US
dc.contributor.authorid59464940200en_US
dc.date.accessioned2025-03-03T07:41:28Z
dc.date.available2025-03-03T07:41:28Z
dc.date.issued2024
dc.description.abstractThis review paper critically examines the recent advancements in refining Generative Adversarial Networks (GANs) to address the challenges posed by small datasets and the persisting issue of texture sticking in the domain of fake license plate recognition. Recognizing the limitations posed by insufficient data, the survey begins with an exploration of various GAN architectures, including pix2pix_GAN, CycleGAN, and SRGAN, that have been employed to synthesize diverse and realistic license plate images. Notable achievements include high accuracy in License Plate Character Recognition (LPCR), advancements in generating new format license plates, and improvements in license plate detection using YOLO. The second focal point of this review centers on mitigating the texture sticking problem, a crucial concern in GAN-generated content. Recent enhancements, such as the integration of StyleGAN2-ADA and StyleGAN3, aim to address challenges related to texture dynamics during video generation. Additionally, adaptive data augmentation mechanisms have been introduced to stabilize GAN training, particularly when confronted with limited datasets. The synthesis of these findings provides a comprehensive overview of the evolving landscape in mitigating challenges associated with small datasets and texture sticking in fake license plate recognition. The review not only underscores the progress made but also identifies emerging trends and areas for future exploration. These insights are vital for researchers, practitioners, and policymakers aiming to bolster the effectiveness and reliability of GAN-based models in the critical domain of license plate recognition. ? by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.48084/etasr.8870
dc.identifier.epage18408
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85211480468
dc.identifier.spage18401
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85211480468&doi=10.48084%2fetasr.8870&partnerID=40&md5=7381389e1d1a0b8ba3cc76ff3db6c882
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36158
dc.identifier.volume14
dc.pagecount7
dc.publisherDr D. Pylarinosen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEngineering, Technology and Applied Science Research
dc.titleAdvancements and Challenges: A Comprehensive Review of GAN-based Models for the Mitigation of Small Dataset and Texture Sticking Issues in Fake License Plate Recognitionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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