On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sensors
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
20
Issue of the periodical within the volume
November
Country of publishing house
CH - SWITZERLAND
Number of pages
24
Pages from-to
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UT code for WoS article
000595052700001
EID of the result in the Scopus database
2-s2.0-85096034594