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FrancoAngeli Journals

Scientific papers

Vol. 47 No. 2 (2023)

Acoustical deep learning methods for road pavement distress recognition

DOI
https://doi.org/10.3280/ria2-2023oa15509
Submitted
February 28, 2023
Published
2024-02-14

Abstract

In the following work, a deep learning-based methodology was proposed to evaluate road surface conditions starting from acoustic signals measured inside the car tire cavity.
The project was carried out in collaboration with Ipool srl., in the context of the SURFAce project, funded by the Tuscany region. Three different classification architectures were proposed: An LSTM, based on the time series of a set of spectral descriptors, and two CNNs, the first focused on the signals’ spectrograms and the second on their Mel-requency cepstral coefficients (MFCCs). The ground truth data set was acquired through a mobile laboratory and classified through aptly developed analysis tools. Two of the three proposed architectures have provided encouraging results, and their implementation on portable systems could lead to real-time pavement classification in a cost and timeefficient way.

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