Deep Learning-Based Turkish License Plate Recognition System on Low-Power Microcontroller Systems

Basit Öğe Kaydı
dc.contributor.authorGorgulu, Emre
dc.contributor.authorÖzcan, Ahmet Remzi
dc.date.accessioned2026-02-08T15:11:12Z
dc.date.available2026-02-08T15:11:12Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 2024-09-21 through 2024-09-22 -- Malatya -- 203423
dc.description.abstractAutomatic License Plate Recognition (ALPR) systems play a crucial role in intelligent transportation systems, traffic management, and security. However, ensuring that these systems operate efficiently on embedded devices with low power consumption presents a significant challenge. In this study, an energy-efficient ALPR system has been designed using the RISC-V-based Kendryte K210 microcontroller. The proposed system adopts a two-stage deep learning-based architecture consisting of YOLOv2 and LPRNet models for license plate detection and recognition. The YOLOv2 model achieves high accuracy in the license plate detection process, while the LPRNet model performs character recognition on the detected license plates. This deep learning-based approach offers an ideal solution for portable and embedded systems with low power consumption. The training of the models leveraged various open-access datasets, aiming to enhance the model's ability to adapt to different conditions. The findings of the study demonstrate that the developed system achieves high accuracy rates in both license plate detection and recognition processes. The performance observed in the license plate detection stage indicates that the system can reliably operate under different conditions. However, the high inference time observed in the license plate recognition process suggests that the model needs to be optimized for real-time applications. In future work, optimization and quantization techniques in deep learning models are planned to enhance the speed and efficiency of the license plate recognition process. Additionally, the applicability of different text recognition models for the license plate recognition problem in embedded systems will be explored. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710693
dc.identifier.isbn9798331531492
dc.identifier.scopus2-s2.0-85207962558
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710693
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5309
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectautomatic license plate recognition
dc.subjectdeep learning
dc.subjectembedded systems
dc.titleDeep Learning-Based Turkish License Plate Recognition System on Low-Power Microcontroller Systems
dc.title.alternativeD s k G l Mikrodenetleyicili Sistemlerde Derin grenme Tabanli T rk Plaka Tanima Sistemi
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu