TY - GEN
T1 - Virtual Sensors Determined Through Machine Learning
AU - Iwashita, Yumi
AU - Stoica, Adrian
AU - Nakashima, Kazuto
AU - Kurazume, Ryo
AU - Torresen, Jim
N1 - Publisher Copyright:
© 2018 TSI Press.
PY - 2018/8/8
Y1 - 2018/8/8
N2 - 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].
AB - 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].
UR - http://www.scopus.com/inward/record.url?scp=85052064772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052064772&partnerID=8YFLogxK
U2 - 10.23919/WAC.2018.8430480
DO - 10.23919/WAC.2018.8430480
M3 - Conference contribution
AN - SCOPUS:85052064772
SN - 9781532377914
VL - 2018-June
T3 - World Automation Congress Proceedings
SP - 318
EP - 321
BT - 2018 World Automation Congress, WAC 2018
PB - IEEE Computer Society
T2 - 2018 World Automation Congress, WAC 2018
Y2 - 3 June 2018 through 6 June 2018
ER -