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

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.