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

Result code in 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>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

20202 - Communication engineering and systems

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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