A preliminary implementation of convolutional neural network in embedded system for road pavement quality classification

Alessio Gagliardi, Vanessa Staderini (Vortragende:r), Sergio Saponara

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in Tagungsband

Abstract

This paper proposes an embedded implementation able to evaluate the pavement quality of road infrastructure by using a low-cost microcontroller board, an analog microphone placed inside the tyre cavity and a Convolutional Neural Network for real-time classification. To train the neural network, tracks audio were collected employing a vehicle moving at different cruise speeds (30, 40, 50 km/h) in the area of Pisa. The raw audio signals were split, labelled and converted into images by calculating the MFCC spectrogram. Finally, the author designed a tiny CNN with a size of 18KB able to classify five different classes: good quality road, bad quality road, pothole-bad road, silence and unknown. The CNN model achieved an accuracy equal to 93.8 % on the original model and about 90 % on the quantized model. The finale embedded system is equipped with BLE communication for the transmission of information to a smartphone equipped with GPS and obtain real-time maps of road quality.
OriginalspracheEnglisch
TitelProceedings of Real-Time Image Processing and Deep Learning 2022
Seiten174-180
Seitenumfang7
DOIs
PublikationsstatusVeröffentlicht - 2022
VeranstaltungSPIE 2022 -
Dauer: 5 Mai 2022 → …

Konferenz

KonferenzSPIE 2022
Zeitraum5/05/22 → …

Research Field

  • Complex Dynamical Systems

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