Abstract
Nowadays, cars host an increasing number of sensors to improve safety, efficiency and comfort. Acoustic sensors have been proposed, in recent works, to acquire information related to the road conditions. Thanks to effectiveness of Deep Learning techniques in analyzing audio data, new scenarios can be envisioned. Based on previous works employing Convolutional Neural Networks (CNN) trained specifically for either of the two tasks, we compare the performance of a CNN trained to jointly classify both wetness and roughness and a transfer learning approach where two CNN, specialized for one task singularly, are joined in a single network. Then we investigate several issues related to the deployment of a classification system able to detect road wetness and roughness on an embedded processor. The first approach seems to score better in our tests and is, thus, selected for the deployment to an ARM-based embedded processor. The computational cost, Real-Time Factor and memory requirements are discussed, as well as the degradation related to the extraction of the features and the weights quantization. Results are promising and show that such an application can be readily deployed on off-the-shelf hardware.
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Acknowledgements
The authors would like to thank ASK industries S.P.A. for financial support and technical assistance. This work is supported by the Italian Ministry of Economic Development (MISE)’s fund for the sustainable growth (F.C.S.) under grant agreement (CUP) B48I15000130008, project VASM (“Vehicle Active Sound Management”). We gratefully acknowledge the support of NVIDIA Corporation forp the donation of the GPU used for this research.
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Pepe, G., Gabrielli, L., Principi, E., Squartini, S., Cattani, L. (2021). Road Type Classification Using Acoustic Signals: Deep Learning Models and Real-Time Implementation. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_4
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