Pavement Condition Detection Method Based on Time-Frequency Features and Capsule Neural Network

Abstract

Pavement condition detection is beneficial to road maintenance and driving experience. Acceleration sensors of smart phones can provide an economical and ubiquitous way to gather pavement condition data. A pavement condition detection method is proposed based on acceleration sensor data of smart phones, which incorporates time-frequency features into capsule networks. The method well addresses the problems of low accuracy caused by the length change of time series, and the high dimensionality of sensor data. Experiments show that the method proposed outperforms the representative methods in accuracy, precision and F1 scores. Especially, it has an improvement in F1 score up to 31.62% compared with the benchmark methods.