Virtual Sensors Determined Through Machine Learning

Yumi Iwashita, Adrian Stoica, Kazuto Nakashima, Ryo Kurazume, Jim Torresen

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

We propose a method that increases the capability of a conventional sensor, transforming it into an enhanced virtual sensor. This paper focuses on a virtual thermal Infrared Radiation (IR) sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classification, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. This paper describes the developed deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree Celsius].

本文言語英語
ホスト出版物のタイトル2018 World Automation Congress, WAC 2018
出版社IEEE Computer Society
ページ318-321
ページ数4
2018-June
ISBN(印刷版)9781532377914
DOI
出版ステータス出版済み - 8 8 2018
イベント2018 World Automation Congress, WAC 2018 - Stevenson, 米国
継続期間: 6 3 20186 6 2018

出版物シリーズ

名前World Automation Congress Proceedings
2018-June
ISSN(印刷版)2154-4824
ISSN(電子版)2154-4832

会議

会議2018 World Automation Congress, WAC 2018
国/地域米国
CityStevenson
Period6/3/186/6/18

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学

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引用スタイル