On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345444" target="_blank" >RIV/68407700:21230/20:00345444 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/s20226425" target="_blank" >https://doi.org/10.3390/s20226425</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s20226425" target="_blank" >10.3390/s20226425</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

Popis výsledku v původním jazyce

The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data.

Název v anglickém jazyce

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

Popis výsledku anglicky

The detection and identification of road anomalies and obstacles in the road infrastructure has been investigated by the research community using different types of sensors. This paper evaluates the detection and identification of road anomalies/obstacles using the data collected from the Inertial Measurement Unit (IMU) installed in a vehicle and in particular from the data generated by the accelerometers’ and gyroscopes’ components. Inspired by the successes of the application of deep learning to various identification problems, this paper investigates the application of Convolutional Neural Network (CNN) to this specific problem. In particular, we propose a novel approach in this context where the time-frequency representation (i.e., spectrogram) is used as an input to the CNN rather than the original time domain data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Svazek periodika

20

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

24

Strana od-do

—

Kód UT WoS článku

000595052700001

EID výsledku v databázi Scopus

2-s2.0-85096034594