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Classification of the acoustics of loose gravel
School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.ORCID iD: 0000-0003-2972-3635
School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0001-7713-8292
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 14, article id 4944Article in journal (Refereed) Published
Abstract [en]

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-inten-sive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
Basel: MDPI, 2021. Vol. 21, no 14, article id 4944
Keywords [en]
Ensemble bagged trees, GoogLeNet, Gravel roads, Loose gravel, Road maintenance, Sound analysis
National Category
Medical Image Processing
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URN: urn:nbn:se:hh:diva-45932DOI: 10.3390/s21144944ISI: 000677020000001PubMedID: 34300684Scopus ID: 2-s2.0-85110519300OAI: oai:DiVA.org:hh-45932DiVA, id: diva2:1616070
Available from: 2021-12-01 Created: 2021-12-01 Last updated: 2022-05-12Bibliographically approved

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Dougherty, Mark

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