Abstract：To ensure road surface quality and safety for pedestrian and drivers， a pavement anomalies detection algorithm based on multi-variate sensor time series data analysis is proposed. In order to realize multi-scale analysis on multi-variate sensor signals collected during driving， wavelet convolutional neural networks and multi-channel networks are used for the anomalies detection. Firstly， convolutional neuron networks are inserted between the multilevel wavelet transforms to analyze the local continuity of a single sensor signal on multi-scale. Secondly， the multi-channel neural network is constructed， of which multi-sensor signals are used as the input of different channels respectively， and the feature vectors of multi-signal are calculated. Finally， multi-layer perceptron is used to detect abnormal road surface according to the output of multi-channel wavelet network. It is shown that the proposed method utilizes the combination of multi-scale analysis， local continuity of signals and combination of multi-variable signals， reduces the false detection rate and missing detection rate， and increases F1 score on the sequential correlative signals.
2019, Vol. 53 
