Detail publikace

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

BRAVENEC, T.

Originální název

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

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

deep learning, neural networks, computer vision, weather classification, machine learning, parallel computing, inference on edge, reduced precision computing

Autoři

BRAVENEC, T.

Vydáno

27. 4. 2021

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5942-7

Kniha

Proceedings I of the 27th Conference STUDENT EEICT 2021

Číslo edice

1

Strany od

1

Strany do

5

Strany počet

5

BibTex

@inproceedings{BUT171849,
  author="Tomáš {Bravenec}",
  title="Multi-Class Weather Classification from Single Images with Convolutional Neural Networks on Embedded Hardware",
  booktitle="Proceedings I of the 27th Conference STUDENT EEICT 2021",
  year="2021",
  number="1",
  pages="1--5",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5942-7"
}