Smartphone Sensor Data Augmentation for Automatic Road Surface Assessment Using a Small Training Dataset

]Smartphones equipped with motion sensors are widely used for data collection in research aimed at the establishment of smart transportation and at, more specifically, automatic road condition assessment. To perform the assessment task, machine learning classifier systems are developed to analyze pa...

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Vydáno v:International Conference on Big Data and Smart Computing s. 239 - 245
Hlavní autoři: Setiawan, Budi Darma, Serdult, Uwe Imre, Kryssanov, Victor
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.01.2021
Témata:
Gallium nitride
Generative adversarial networks
road surface assessment
Roads
Smart phones
smartphone data augmentation
Task analysis
Training
Tuning
unrolled GAN
ISSN:2375-9356
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Shrnutí:]Smartphones equipped with motion sensors are widely used for data collection in research aimed at the establishment of smart transportation and at, more specifically, automatic road condition assessment. To perform the assessment task, machine learning classifier systems are developed to analyze patterns of vibration signals recorded from a driver's smartphone. Obtaining a balanced training dataset required for the classifier system to work properly is, however, a difficult task. The presented study develops an approach based on an Unrolled Generative Adversarial Network (Unrolled GAN) to produce synthetic data for balancing the training dataset. Experiments conducted in the study demonstrated that the approach allows for generating high-quality synthetic data as long as the unrolled GAN are kept controlled to balance the discriminator and generator modules of the networks.
ISSN:2375-9356
DOI:10.1109/BigComp51126.2021.00052