In recent years, a lot of effort has been put in vehicle safety systems for manned and unmanned driving. Road conditions are crucial among the factors that influence the choice of the driving style and the safety systems. A few works based the detection of the road condition on acoustic sensors mounted on the vehicle using deep learning techniques. In this work we enhance the state of the art by introducing a Siamese Convolutional Neural Network architecture able to achieve improved results for the classification of the road surface roughness. A new dataset is recorded and the approach is tested, achieving a best overall F1-score of 95.6%, improving by 14% the results of the previous method.
Processing Acoustic Data with Siamese Neural Networks for Enhanced Road Roughness Classification / Gabrielli, L.; Ambrosini, L.; Vesperini, F.; Bruschi, Valeria; Squartini, S.; Cattani, L.. - ELETTRONICO. - 2019-:(2019), pp. 1-7. (Intervento presentato al convegno 2019 International Joint Conference on Neural Networks, IJCNN 2019 tenutosi a hun nel 2019) [10.1109/IJCNN.2019.8852108].
Processing Acoustic Data with Siamese Neural Networks for Enhanced Road Roughness Classification
Gabrielli L.;Ambrosini L.;Vesperini F.;BRUSCHI, Valeria;Squartini S.;
2019-01-01
Abstract
In recent years, a lot of effort has been put in vehicle safety systems for manned and unmanned driving. Road conditions are crucial among the factors that influence the choice of the driving style and the safety systems. A few works based the detection of the road condition on acoustic sensors mounted on the vehicle using deep learning techniques. In this work we enhance the state of the art by introducing a Siamese Convolutional Neural Network architecture able to achieve improved results for the classification of the road surface roughness. A new dataset is recorded and the approach is tested, achieving a best overall F1-score of 95.6%, improving by 14% the results of the previous method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
