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首页> 外文期刊>Sensors Journal, IEEE >Deep Sensing Approach to Single-Sensor Vehicle Weighing System on Bridges
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Deep Sensing Approach to Single-Sensor Vehicle Weighing System on Bridges

机译：桥梁单传感器车辆称重系统的深度传感方法

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摘要

Bridge weigh-in-motion (BWIM) is a technique for detecting overloaded vehicles crossing a bridge without requiring them to stop. It may also be useful for monitoring the structural health of the bridge itself. To achieve accurate weighing of each vehicle, its properties, such as speed, locus, and wheel positions, should be estimated in advance. Conventionally, such information has been obtained via additional sensors such as cameras or via peak-signal detection, using multiple sensors installed across the bridge. This may require substantial computational resources or expensive synchronization between sensors, and the complexity of the overall BWIM system may lead to frequent breakdowns. In this paper, we propose a single-sensor-based BWIM system that utilizes a deep neural network. First, a vehicle’s properties are obtained via feature extraction from the bridge strain response, as sampled by a single strain sensor. BWIM is then performed, using the same response data. The model parameters for vehicle detection are optimized automatically by consulting a surveillance camera while obtaining ground-truth data for a large number of vehicles crossing the bridge. After the model is optimized for the target bridge, the camera may be removed. Our proposal paves the way toward low-cost, compact, and single-sensor BWIM systems.
机译：桥梁动态称重（BWIM）是一种用于检测超载车辆通过桥梁而无需停车的技术。它对于监视桥梁本身的结构健康状况也可能有用。为了准确地称量每辆车，应预先估算其性能，例如速度，轨迹和车轮位置。常规上，使用跨桥安装的多个传感器通过附加的传感器（例如摄像机）或峰值信号检测获得此类信息。这可能需要大量的计算资源或传感器之间昂贵的同步，并且整个BWIM系统的复杂性可能导致频繁的故障。在本文中，我们提出了一种利用深度神经网络的基于单传感器的BWIM系统。首先，通过从桥梁应变响应中提取特征来获得车辆的特性，如单个应变传感器所采样的那样。然后，使用相同的响应数据执行BWIM。用于车辆检测的模型参数是通过咨询监控摄像头自动优化的，同时获得了大量过桥车辆的地面数据。在针对目标桥优化模型之后，可以将摄像机卸下。我们的建议为低成本，紧凑和单传感器BWIM系统铺平了道路。

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