Multi-Class Weather Classification from Single Images with Convolutional Neural Networks on Embedded Hardware

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141054" target="_blank" >RIV/00216305:26220/21:PU141054 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Class Weather Classification from Single Images with Convolutional Neural Networks on Embedded Hardware

Popis výsledku v původním jazyce

The paper is focused on creating a lightweight machine learning solution for classification of weather conditions from input images, that can process the input data in real time on embedded devices. The approach to the classification uses deep convolutional neural networks architecture with focus on lightweight design and fast inference, while providing high accuracy results. The focus on creating lightweight convolutional neural network architecture capable of classification of weather conditions also enables usage of the network in real time applications at the edge.

Název v anglickém jazyce

Multi-Class Weather Classification from Single Images with Convolutional Neural Networks on Embedded Hardware

Popis výsledku anglicky

The paper is focused on creating a lightweight machine learning solution for classification of weather conditions from input images, that can process the input data in real time on embedded devices. The approach to the classification uses deep convolutional neural networks architecture with focus on lightweight design and fast inference, while providing high accuracy results. The focus on creating lightweight convolutional neural network architecture capable of classification of weather conditions also enables usage of the network in real time applications at the edge.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů