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PŘINOSIL, J.; KŘÍŽ, P.
Originální název
License Plate Recognition on Low-Cost Devices
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
This paper presents a comprehensive approach to vehicle license plate recognition running on low-cost devices. Leveraging convolutional neural networks, we evaluate models like YOLOv7-tiny and YuNet for license plate detection, favoring YuNet's 1080×1080 resolution for the accuracy-computation trade-off. For license plate character recognition, we proposed a YOLOv4-tiny derivation model, achieving good accuracy and fast computation. The proposed approaches were validated on a test set from real traffic using Raspberry Pi 4 as the target computational device.
Anglický abstrakt
Klíčová slova
license plate recognition; image analysis; convolutional neural networks; YOLO; YuNet
Klíčová slova v angličtině
Autoři
Rok RIV
2024
Vydáno
29.10.2023
Nakladatel
IEEE Computer Society
ISBN
979-8-3503-9328-6
Kniha
2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
Strany od
28
Strany do
32
Strany počet
5
BibTex
@inproceedings{BUT185598, author="Jiří {Přinosil} and Petr {Kříž}", title="License Plate Recognition on Low-Cost Devices", booktitle="2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)", year="2023", pages="28--32", publisher="IEEE Computer Society", isbn="979-8-3503-9328-6" }