КФУ. Карточка публикации. Traffic Sign Recognition System Based on Neural Network Algorithm

КАЗАНСКИЙ
ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ

RUS

- EN
- 中文
- ES

TRAFFIC SIGN RECOGNITION SYSTEM BASED ON NEURAL NETWORK ALGORITHM

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2025

Язык

английский

Авторы, сотрудники КФУ

Ахмедова Альфира Мазитовна, автор

Жажнева Ирина Васильевна, автор

Авторы, студенты КФУ

Габитова Айгуль Ирековна, автор

Библиографическое описание на языке оригинала

Gabitova A, Akhmedova A, Zhazhneva I., Traffic Sign Recognition System Based on Neural Network Algorithm//Proceedings - 2025 International Russian Smart Industry Conference, SmartIndustryCon 2025. - 2025. - Vol., Is.. - P.488-493.

Аннотация

2025 International Russian Smart Industry Conference (SmartIndustryCon)

Ключевые слова

recognition, convolutional neural networks, telegram bot, traffic signs, RTSD

Название журнала

2025 International Russian Smart Industry Conference (SmartIndustryCon)

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007157577&doi=10.1109%2fSmartIndustryCon65166.2025.10985965&partnerID=40&md5=540897c6eebaf098a6537c3642ce8857

Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку

https://repository.kpfu.ru/?p_id=315531

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Ахмедова Альфира Мазитовна	ru_RU
dc.contributor.author	Жажнева Ирина Васильевна	ru_RU
dc.contributor.author	Габитова Айгуль Ирековна	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Gabitova A, Akhmedova A, Zhazhneva I., Traffic Sign Recognition System Based on Neural Network Algorithm//Proceedings - 2025 International Russian Smart Industry Conference, SmartIndustryCon 2025. - 2025. - Vol., Is.. - P.488-493.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=315531	ru_RU
dc.description.abstract	2025 International Russian Smart Industry Conference (SmartIndustryCon)	ru_RU
dc.description.abstract	This paper presents the implementation of a system for automatic recognition of traffic signs using convolutional neural networks (CNN). The solution is based on the ResNet architecture with elements of recurrent layers, which increases the accuracy and versatility of image processing. Traffic sign recognition system provides user functionality to upload images of signs to the telegram bot, receive their classification, as well as useful tips on traffic rules. Development was performed using the PyTorch library, RTD dataset, and DeepPavlov language model. Test results showed high model accuracy (97.7% accuracy) and user-friendliness of the interface. The developed solution can be used both in educational institutions for driver training and as an auxiliary tool for analyzing the traffic situation	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	recognition	ru_RU
dc.subject	convolutional neural networks	ru_RU
dc.subject	telegram bot	ru_RU
dc.subject	traffic signs	ru_RU
dc.subject	RTSD	ru_RU
dc.title	Traffic Sign Recognition System Based on Neural Network Algorithm	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU