Real-time vehicle identification using two-step LSTM method for acceleration-based bridge weigh-in-motion system
Real-time vehicle identification using two-step LSTM method for acceleration-based bridge weigh-in-motion system
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DOI：
10.1007/s13349-022-00576-2
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发表时间：
2022-05
影响因子：
4.4
通讯作者：
Yanjie Zhu;Hidehiko Sekiya;Takayuki Okatani;I. Yoshida;Shuichi Hirano
中科院分区：
文献类型：
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作者：
Yanjie Zhu;Hidehiko Sekiya;Takayuki Okatani;I. Yoshida;Shuichi Hirano
近年来，加速度计已被用于桥梁动态称重（BWIM）系统，以提供比传统的基于应变的传感器更持久的现场测量。准确的车辆识别是BWIM系统的基础，为车辆载荷监测和超重交通检测提供了基础支持。然而，在真实的时间轴识别的研究工作仍然不足，特别是基于加速度计的BWIM系统。在本文中，我们提出了一个两步的解决方案，实时车辆识别设计的加速度测量。在该方法中，构建序列到标签长短期记忆（LSTM）网络来直接识别多车道系统中的轴诱导反应。输入序列是对原始数据进行小波变换后的小波系数。基于可靠的车轴识别结果，提出了一种自动分组步骤，并应用于车型识别。模型训练和方法评估进行现场测量，从东京的公路桥。使用两个数据集，即，456轴车辆191辆，1380轴车辆596辆。结果表明，使用所提出的LSTM方法可以从两个数据集正确识别98%的车轴，而车型识别的准确率为96%，这可以证明所提出的方法的鲁棒性。此外，所有被检测车辆的行驶车道检测均为100%，无任何失败案例。与直接使用加速度测量作为输入源的一体化深度网络相比，所提出的两步LSTM方法需要更少的训练数据，因此它是一种计算效率高的解决方案，这将使其具有推广能力，可应用于其他桥梁。
Recently, accelerometers have been employed for bridge weigh-in-motion (BWIM) systems to provide more durable field measurements comparing with conventional strain-based sensors. As the basis of BWIM system, accurate vehicle identification provides fundamental support for vehicle loads monitoring and overweight traffic detection. However, research efforts on axle recognition in real time are still inadequate, especially for accelerometer-based BWIM system. In this paper, we propose a two-step solution for real-time vehicle identification designed for acceleration measurements. In this method, a sequence-to-label long–short-term memory (LSTM) network is constructed to identify axle-induced responses in a multi-lane system directly. The input sequence is wavelet coefficients after performing wavelet transform on the raw data. Based on the trustworthy axle identification results, an auto-grouping step is then proposed and applied for vehicle-type identification. Model training and method evaluation are conducted using filed measurements from a highway bridge in Tokyo. Two data sets are utilized, i.e., 191 vehicles with 456 axles and 596 vehicles with 1380 axles. Results show that 98% axles can be identified correctly using proposed LSTM method from both data sets, while accuracy of vehicle-type identification is 96% for both data sets, which can demonstrate the robustness of proposed methods. Moreover, the driving lane detection of all detected vehicles is 100% without any failed cases. Comparing with all-in-one deep network using acceleration measurements as input sources directly, the proposed two-step LSTM method requires less training data, hence it is a computationally efficient solution, which would enable its generalization capability for applying on other bridges.