Název:
Multi-Class Weather Classification From Single Images With Convolutional Neural Networks On Embedded Hardware
Autoři:
Bravenec, Tomáš Typ dokumentu: Příspěvky z konference
Jazyk:
eng
Nakladatel: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstrakt:
The paper is focused on creating a lightweight machine learning solution for classificationof weather conditions from input images, that can process the input data in real time on embeddeddevices. The approach to the classification uses deep convolutional neural networks architecture withfocus on lightweight design and fast inference, while providing high accuracy results. The focus oncreating lightweight convolutional neural network architecture capable of classification of weatherconditions also enables usage of the network in real time applications at the edge.
Klíčová slova:
computer vision; deep learning; inference on edge; machine learning,parallel computing; neural networks; reduced precision computing; weather classification Zdrojový dokument: Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers, ISBN 978-80-214-5942-7
Instituce: Vysoké učení technické v Brně
(web)
Informace o dostupnosti dokumentu:
Plný text je dostupný v Digitální knihovně VUT. Původní záznam: http://hdl.handle.net/11012/200700