Vehicle identification using deep learning for expressway monitoring based on ultra-weak FBG arrays

Abstract

A deep learning with knowledge distillation scheme for lateral lane-level vehicle identification based on ultra-weak fiber Bragg grating (UWFBG) arrays is proposed. Firstly, the UWFBG arrays are laid underground in each expressway lane to obtain the vibration signals of vehicles. Then, three types of vehicle vibration signals (the vibration signal of a single vehicle, the accompanying vibration signal, and the vibration signal of laterally adjacent vehicles) are separately extracted by density-based spatial clustering of applications with noise (DBSCAN) to produce a sample library. Finally, a teacher model is designed with a residual neural network (ResNet) connected to a long short-term memory (LSTM), and a student model consisting of only one LSTM layer is trained by knowledge distillation (KD) to satisfy the real-time monitoring with high accuracy. Experimental demonstration verifies that the average identification rate of the student model with KD is 95% with good real-time capability. By comparison tests with other models, the proposed scheme shows a solid performance in the integrated evaluation for vehicle identification.