Opt Express. 2023 May 8;31(10):16754-16769. doi: 10.1364/OE.487400.
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.
提出了一种基于超弱光纤布拉格光栅（UWFBG）阵列的深度学习与知识蒸馏方案，用于进行车道路侧车道级别的车辆识别。首先，UWFBG 阵列铺设在每条高速公路车道的地下，以获取车辆的振动信号。然后，通过基于密度的带有噪声的应用空间聚类（DBSCAN）分别提取三种类型的车辆振动信号（单个车辆的振动信号、伴随振动信号和侧向相邻车辆的振动信号），以生成样本库。最后，设计了一个带有残差神经网络（ResNet）连接长短期记忆（LSTM）的教师模型，并通过知识蒸馏（KD）训练仅由一个 LSTM 层组成的学生模型，以满足高精度的实时监测需求。实验验证表明，KD 学生模型的平均识别率为 95%，具有良好的实时性能。通过与其他模型的对比测试，所提出的方案在车辆识别的综合评估中表现出了稳健的性能。
