Smart parking system for license plate recognition based on YOLO neural network and optical character recognition

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dc.citation.epage129
dc.citation.issue3
dc.citation.journalTitleКомп’ютерні системи проектування. Теорія і практика
dc.citation.spage123
dc.contributor.affiliationНаціональний університет "Львівська політехніка"
dc.contributor.affiliationНаціональний університет "Львівська політехніка"
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorВисоцький, Владислав
dc.contributor.authorЯворський, Назарій
dc.contributor.authorVysotskyi, Vladyslav
dc.contributor.authorJaworski, Nazariy
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-12-16T08:40:53Z
dc.description.abstractУ статті описано метод розпізнавання номерних знаків на прикладі навчання та розгортання моделі машинного навчання. У дослідженні використано архітектуру нейронної мережі YOLO (You Only Look Once‖) і методи оптичного розпізнавання символів (OCR) для вилучення символів номерних знаків, що уможливлюють розпізнавання номерних знаків у реальному часі. Експериментальні випробування, які охоплюють навчання моделі, валідацію та оцінку, продемонст- рували ефективність цих методів для поліпшення автоматизованого контролю доступу в розумних системах паркування.
dc.description.abstractThis paper describes a license plate recognition method, exemplified by training and deploying a machine learning model. The study uses the YOLO (You Only Look Once‖) neural network architecture and optical character recognition (OCR) techniques to extract license plate characters for real-time license plate recognition. Experimental tests, including model training, validation, and evaluation, demonstrate the effectiveness of these methods in enhancing automated access control in smart parking systems.
dc.format.extent123-129
dc.format.pages7
dc.identifier.citationVysotskyi V. Smart parking system for license plate recognition based on YOLO neural network and optical character recognition / Vladyslav Vysotskyi, Nazariy Jaworski // Computer Systems of Design. Theory and Practice. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 6. — No 3. — P. 123–129.
dc.identifier.citation2015Vysotskyi V., Jaworski N. Smart parking system for license plate recognition based on YOLO neural network and optical character recognition // Computer Systems of Design. Theory and Practice, Lviv. 2024. Vol 6. No 3. P. 123–129.
dc.identifier.citationenAPAVysotskyi, V., & Jaworski, N. (2024). Smart parking system for license plate recognition based on YOLO neural network and optical character recognition. Computer Systems of Design. Theory and Practice, 6(3), 123-129. Lviv Politechnic Publishing House..
dc.identifier.citationenCHICAGOVysotskyi V., Jaworski N. (2024) Smart parking system for license plate recognition based on YOLO neural network and optical character recognition. Computer Systems of Design. Theory and Practice (Lviv), vol. 6, no 3, pp. 123-129.
dc.identifier.doihttps://doi.org/10.23939/cds2024.03.123
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/124087
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofКомп’ютерні системи проектування. Теорія і практика, 3 (6), 2024
dc.relation.ispartofComputer Systems of Design. Theory and Practice, 3 (6), 2024
dc.relation.references[1] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, ―YouO nly Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA,2016, pp. 779–788, https://doi.org/10.48550/arXiv.1506.02640
dc.relation.references[2] [Electronic resource]. Smart Parking Market Size is projected to reach USD 16.54 Billion by 2030, growing at a CAGR of 13.6 %: Straits Research,https://www.globenewswire.com/en/newsrelease/2023/09/14/2743480/0/en/Smart-Parking-Market-Size-is-projected-to-reach-USD-16-54-Billion-by-2030- growing-at-a-CAGR-of-13-6-Straits-Research.html
dc.relation.references[3] [Electronic resource]. AutoRia Dataset, https://nomeroff.net.ua/datasets
dc.relation.references[4] [Electronic resource]. Ultralytics YOLOv8 Docs, https://docs.ultralytics.com/models/yolov8
dc.relation.references[5] [Electronic resource]. Roboflow Documentation, https://docs.roboflow.com
dc.relation.references[6] [Electronic resource]. CUDA Toolkit Documentation, https://docs.nvidia.com/cuda/
dc.relation.references[7] Afif, Mouna & Said, Yahia & Atri, Mohamed (2020). Computer vision algorithms acceleration using graphic processors NVIDIA CUDA. Cluster Computing, 23. 10.1007/s10586-020-03090-6.
dc.relation.references[8] [Electronic resource]. Sambasivarao K., ―Non-maximum Suppression (NMS), A technique to filter the predictions of object detectors‖, Towards Data Science, https://towardsdatascience.com/non-maximum-suppressionnms-93ce178e177c
dc.relation.references[9] [Electronic resource]. EasyOCR Github page, https://github.com/JaidedAI/EasyOCR
dc.relation.references[10] [Electronic resource]. LiteRT overview, https://ai.google.dev/edge/litert
dc.relation.referencesen[1] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, ―YouO nly Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA,2016, pp. 779–788, https://doi.org/10.48550/arXiv.1506.02640
dc.relation.referencesen[2] [Electronic resource]. Smart Parking Market Size is projected to reach USD 16.54 Billion by 2030, growing at a CAGR of 13.6 %: Straits Research,https://www.globenewswire.com/en/newsrelease/2023/09/14/2743480/0/en/Smart-Parking-Market-Size-is-projected-to-reach-USD-16-54-Billion-by-2030- growing-at-a-CAGR-of-13-6-Straits-Research.html
dc.relation.referencesen[3] [Electronic resource]. AutoRia Dataset, https://nomeroff.net.ua/datasets
dc.relation.referencesen[4] [Electronic resource]. Ultralytics YOLOv8 Docs, https://docs.ultralytics.com/models/yolov8
dc.relation.referencesen[5] [Electronic resource]. Roboflow Documentation, https://docs.roboflow.com
dc.relation.referencesen[6] [Electronic resource]. CUDA Toolkit Documentation, https://docs.nvidia.com/cuda/
dc.relation.referencesen[7] Afif, Mouna & Said, Yahia & Atri, Mohamed (2020). Computer vision algorithms acceleration using graphic processors NVIDIA CUDA. Cluster Computing, 23. 10.1007/s10586-020-03090-6.
dc.relation.referencesen[8] [Electronic resource]. Sambasivarao K., ―Non-maximum Suppression (NMS), A technique to filter the predictions of object detectors‖, Towards Data Science, https://towardsdatascience.com/non-maximum-suppressionnms-93ce178e177c
dc.relation.referencesen[9] [Electronic resource]. EasyOCR Github page, https://github.com/JaidedAI/EasyOCR
dc.relation.referencesen[10] [Electronic resource]. LiteRT overview, https://ai.google.dev/edge/litert
dc.relation.urihttps://doi.org/10.48550/arXiv.1506.02640
dc.relation.urihttps://www.globenewswire.com/en/newsrelease/2023/09/14/2743480/0/en/Smart-Parking-Market-Size-is-projected-to-reach-USD-16-54-Billion-by-2030-
dc.relation.urihttps://nomeroff.net.ua/datasets
dc.relation.urihttps://docs.ultralytics.com/models/yolov8
dc.relation.urihttps://docs.roboflow.com
dc.relation.urihttps://docs.nvidia.com/cuda/
dc.relation.urihttps://towardsdatascience.com/non-maximum-suppressionnms-93ce178e177c
dc.relation.urihttps://github.com/JaidedAI/EasyOCR
dc.relation.urihttps://ai.google.dev/edge/litert
dc.rights.holder© Національний університет „Львівська політехніка“, 2024
dc.rights.holder© Vysotskyi V., Jaworski N., 2024
dc.subjectмоделі нейронних мереж
dc.subjectYOLO
dc.subjectрозпізнавання номерних знаків
dc.subjectрозумне паркування
dc.subjectоптичне розпізнавання символів
dc.subjectneural network models
dc.subjectYOLO
dc.subjectlicense plates recognition
dc.subjectsmart parking
dc.subjectoptical character recognition
dc.titleSmart parking system for license plate recognition based on YOLO neural network and optical character recognition
dc.title.alternativeСистема розумного паркування для розпізнавання номерних знаків на основі нейромережі YOLO та оптичного розпізнавання символів
dc.typeArticle

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Комп'ютерні системи проектування теорія і практика. – 2024. – Том 6, № 3