The current roadway monitoring is expensive and not systematic. This paper proposes a new system able to evaluate the pavement quality of road infrastructure. The embedded system records and processes the acoustic data of the wheel-road interaction and classifies in real-time roadways' health thanks to integrated AI solutions. The measurements to produce the dataset to train a convolutional neural network (CNN) were collected using a vehicle operating at different cruise speeds in the area of Pisa. The dataset is composed by acoustic data belonging to several typologies of roads: dirty or grass roads, high roughness surfaces and roads with cracks or potholes. The raw audio signals were split, labelled, and converted into images by calculating the Mel spectrogram. Finally, the authors designed a tiny CNN with a size equal to 18 kB able to classify between four different classes: good quality road, ruined road, silence and unknown. The CNN architecture achieves an accuracy of about 93% on the original model and 90% on the quantized one. Quantization permits to convert the final architecture into a suitable form to be deployed on a low-complex embedded system integrated in the tyre cavity. In addition, a custom board was designed to act as IoT node thanks to a Bluetooth Low Energy communication towards smartphones and/or car infotainment systems. These systems, featured with GPS, guarantee to obtain real-time maps service of road quality. At authors' knowledge, this is the first real-time and fully integrated solution at the state of the art for road pavement quality analysis and classification on acoustic data.INDEX TERMS road surface classification; convolutional neural network; audio processing; embedded system; image recognition.

No abstract

No abstract