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Система распознавания Ð·Ð°Ð¿Ñ€ÐµÑ‰Ð°ÑŽÑ‰Ð¸Ñ Ð´Ð¾Ñ€Ð¾Ð¶Ð½Ñ‹Ñ Ð·Ð½Ð°ÐºÐ¾Ð² с использованием компьютерного зрения

выпускная квалификационная работа бакалавра

Система распознавания Ð·Ð°Ð¿Ñ€ÐµÑ‰Ð°ÑŽÑ‰Ð¸Ñ Ð´Ð¾Ñ€Ð¾Ð¶Ð½Ñ‹Ñ Ð·Ð½Ð°ÐºÐ¾Ð² с использованием компьютерного зрения

Abstract

Объект исследования – системы компьютерного зрения. Цель работы – разработка системы компьютерного зрения для распознавания запрещающих дорожных знаков, соответствующих требованиям венской конвенции о дорожном движении. В работе рассмотрены существующие алгоритмы компьютерного зрения и машинного обучения. Разработано программное обеспечение системы распознавания запрещающих дорожных знаков, состоящее из детектора и классификатора. Рассмотрены средства вычислительной техники, подходящие для использования их в качестве основы для бортовой системы компьютерного зрения и в качестве ускорителей вычислений. Разработанное программное обеспечение развернуто и протестировано на предлагаемом аппаратном обеспечении. Определены основные параметры разработанной системы. Проведен анализ полученных результатов. Предложены методы увеличения производительности разработанной системы распознавания запрещающих дорожных знаков.

The object of the graduate qualification work is computer vision systems. The subject of the graduate qualification work is the development a computer vision system for recognizing prohibitory traffic signs that comply with the requirements of the Vienna Convention on Road Signs and Signals. In this work existing computer vision and machine learning algorithms were considered. Prohibitory road signs detector and classifier software were developed. Hardware devices suitable for use as a basis for an onboard computer vision system and as computational accelerators were surveyed. The developed software was deployed and tested on the proposed hardware. The main parameters of the developed system were determined. The analysis of the obtained results was carried out. Methods for increasing the performance of the developed prohibitory traffic signs recognition system were proposed.

Keywords

machine learning, метод градиентного спуска, компьютерное зрение, машинное обучение, computer vision, gradient descent algorithm

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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