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FrancoAngeli Journals

Articoli Scientifici

V. 47 N. 2 (2023)

Metodi di deep learning acustico per il riconoscimento dei dissesti della pavimentazione stradale

DOI
https://doi.org/10.3280/ria2-2023oa15509
Inviata
28 febbraio 2023
Pubblicato
14-02-2024

Abstract

Nel seguente lavoro è stata proposta una metodologia basata su tecniche di deep learning per la valutazione delle condizioni della superficie stradale a partire da segnali acustici misurati all’interno della cavità dello pneumatico. Il progetto è stato svolto in collaborazione con Ipool srl., nel contesto del progetto SURFAce, finanziato dalla regione Toscana. Sono state proposte tre architetture di classificazione: una LSTM (Long short-term memory network) basata sull’andamento temporale di un insieme di descrittori spettrali e due CNN (Convolutional neural network), una incentrata sugli spettrogrammi dei segnali, l’altra sui Mel-frequency cepstral coefficients (MFCC). Il dataset di ground truth è stato acquisito tramite un laboratorio mobile e classificato mediante strumenti di analisi appositamente sviluppati. Due delle tre architetture proposte hanno fornito risultati incoraggianti. L’implementazione di tali strumenti su dispositivi mobili potrebbe rendere possibile la classificazione dello stato della pavimentazione in tempo reale con ridotti costi economici e temporali.

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